Дисертації: "Recommender Algorithm"

1

ROSSETTI, MARCO. "Advancing Recommender Systems from the Algorithm, Interface and Methodological Perspective." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/70560.

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I sistemi di raccomandazione sono componenti software che aiutano gli utenti a trovare quello che stanno cercando. I sistemi di raccomandazione sono stati applicati a diverse aree, dal commercio elettronico alle notizie, dalla musica al turismo, sfruttando tutte le informazioni disponibili per imparare le preferenze dell’utente e fornire raccomandazioni utili. La vasta area dei sistemi di raccomandazione riguarda molte tematiche che richiedono una conoscenza profonda e grandi sforzi di ricerca. In particolare, tre aspetti principali sono: algoritmi, ossia i componenti intelligenti che elaborano le raccomandazioni; interfacce, ossia gli strumenti che permettono di mostrare le raccomandazioni agli utenti; valutazione, ossia le metodologie per validare l’efficacia dei sistemi di raccomandazione. In questa dissertazione ci focalizziamo su questi aspetti guidati da tre considerazioni. Primo, il contenuto testuale relativo agli item e ai rating può essere sfruttato per migliorare diversi aspetti, come elaborare raccomandazioni, fornire spiegazioni e comprendere i gusti degli utenti e le potenzialità degli item. Secondo, il tempo nei sistemi di raccomandazione dovrebbe essere considerato in quanto ha una grande influenza sulla popolarità e sui gusti. Terzo, i protocolli di valutazione offline non sono completamente convincenti, in quanto si basano su statistiche di accuratezza che non sempre rispecchiano le reali preferenze dell’utente. Date le motivazioni citate, vengono forniti sei contributi divisi tra l’integrazione di concetti e tempo nei sistemi di raccomandazione, l’applicazione del topic model per analizzare recensioni e spiegare fattori latenti, e la validazione delle misure di valutazione offline.
Recommender systems are software components that assist users in finding what they are looking for. They have been applied to all kinds of domains, from ecommerce to news, from music to tourism, exploiting all the information available in order to learn user's preferences and to provide useful recommendations. The broad area of recommender systems has many topics that require a deep understanding and great research efforts. In particular, three main aspects are: algorithms, which are the hidden intelligent components that compute recommendations; interfaces, which are the way in which recommendations are shown to the user; evaluation, which is the methodology to assess the effectiveness of a recommender system. In this dissertation we focus on these aspects guided by three considerations. First, textual content related to items and ratings can be exploited in order to improve several aspects, such as to compute recommendations, provide explanations, understand user's tastes and item's capabilities. Second, time in recommender systems should be considered as it has a great influence on popularity and tastes. Third, offline evaluation protocols are not fully convincing, as they are based on accuracy statistics that do not always reflect real user's preferences. Following these motivations six contributions have been delivered, broadly divided in the integration of concepts and time in recommender systems, the application of the topic model to analyze user reviews and to explain latent factors, and the validation of offline recommendation accuracy measurements.

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2

NARAYANASWAMY, SHRIRAM. "A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1186165016.

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3

Bora, Prachi Champalal. "Runtime Algorithm Selection For Grid Environments: A Component Based Framework." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/33823.

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Grid environments are inherently heterogeneous. If the computational power provided by collaborations on the Grid is to be harnessed in the true sense, there is a need for applications that can automatically adapt to changes in the execution environment. The application writer should not be burdened with the job of choosing the right algorithm and implementation every time the resources on which the application runs are changed. A lot of research has been done in adapting applications to changing conditions. The existing systems do not address the issue of providing a unified interface to permit algorithm selection at runtime. The goal of this research is to design and develop a unified interface to applications in order to permit seamless access to different algorithms providing similar functionalities. Long running, computationally intensive scientific applications can produce huge amounts of performance data. Often, this data is discarded once the applicationâ s execution is complete. This data can be utilized in extracting information about algorithms and their performance. This information can be used to choose algorithms intelligently. The research described in this thesis aims at designing and developing a component based unified interface for runtime algorithm selection in grid environments. This unified interface is necessary so that the application code does not change if a new algorithm is used to solve the problem. The overhead associated with making the algorithm choice transparent to the application is evaluated. We use a data mining approach to algorithm selection and evaluate its potential effectiveness for scientific applications.
Master of Science

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4

Bora, Prachi. "Runtime Algorithm Selection For Grid Environments: A Component Based Framework." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/33823.

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Grid environments are inherently heterogeneous. If the computational power provided by collaborations on the Grid is to be harnessed in the true sense, there is a need for applications that can automatically adapt to changes in the execution environment. The application writer should not be burdened with the job of choosing the right algorithm and implementation every time the resources on which the application runs are changed. A lot of research has been done in adapting applications to changing conditions. The existing systems do not address the issue of providing a unified interface to permit algorithm selection at runtime. The goal of this research is to design and develop a unified interface to applications in order to permit seamless access to different algorithms providing similar functionalities. Long running, computationally intensive scientific applications can produce huge amounts of performance data. Often, this data is discarded once the applicationâ s execution is complete. This data can be utilized in extracting information about algorithms and their performance. This information can be used to choose algorithms intelligently. The research described in this thesis aims at designing and developing a component based unified interface for runtime algorithm selection in grid environments. This unified interface is necessary so that the application code does not change if a new algorithm is used to solve the problem. The overhead associated with making the algorithm choice transparent to the application is evaluated. We use a data mining approach to algorithm selection and evaluate its potential effectiveness for scientific applications.
Master of Science

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5

Zhang, Richong. "Probabilistic Approaches to Consumer-generated Review Recommendation." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/19935.

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Consumer-generated reviews play an important role in online purchase decisions for many consumers. However, the quality and helpfulness of online reviews varies significantly. In addition, the helpfulness of different consumer-generated reviews is not disclosed to consumers unless they carefully analyze the overwhelming number of available contents. Therefore, it is of vital importance to develop predictive models that can evaluate online product reviews efficiently and then display the most useful reviews to consumers, in order to assist them in making purchase decisions. This thesis examines the problem of building computational models for predicting whether a consumer-generated review is helpful based on consumers' online votes on other reviews (where a consumer's vote on a review is either HELPFUL or UNHELPFUL), with the aim of suggesting the most suitable products and vendors to consumers.In particular, we propose in this thesis three different helpfulness prediction approaches for consumer-generated reviews. Our entropy-based approach is relatively simple and suitable for applications requiring simple recommendation engine with fully-voted reviews. However, our entropy-based approach, as well as the existing approaches, lack a general framework and are all limited to utilizing fully-voted reviews. We therefore present a probabilistic helpfulness prediction framework to overcome these limitations. To demonstrate the versatility and flexibility of this framework, we propose an EM-based model and a logistic regression-based model. We show that the EM-based model can utilize reviews voted by a very small number of voters as the training set, and the logistic regression-based model is suitable for real-time helpfulness predicting of consumer-generated reviews. To our best knowledge, this is the first framework for modeling review helpfulness and measuring the goodness of models. Although this thesis primarily considers the problem of review helpfulness prediction, the presented probabilistic methodologies are, in general, applicable for developing recommender systems that make recommendation based on other forms of user-generated contents.

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6

Ye, Brian, and Benny Tieu. "Implementation and Evaluation of a Recommender System Based on the Slope One and the Weighted Slope One Algorithm." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166438.

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Recommender systems are used on many different websites today and are mechanisms that are supposed to accurately give personalized recommendations of items to a set of different users. An item can for example be movies on Netflix. The purpose of this paper is to implement an algorithm that fulfills five stated goals of the implementation. The goals are as followed: the algorithm should be easy to implement, be effective on query time, accurate on recommendations, put little expectations on users and alternations of algorithm should not have to be changed comprehensively. Slope One is a simplified version of linear regression and can be used to recommend items. By using the Netflix Prize data set from 2009 and the Root-Mean-Square-Error (RMSE) as an evaluator, Slope One generates an accuracy of 1.007 units. The Weighted Slope One, which takes the relevancy of items into the calculation, generates an accuracy of 0.990 units. Adding Weighted Slope One to the Slope One implementation can be done without changing the fundamentals of the Slope One algorithm. It is nearly instantaneous to generate a recommendation of a movie with regular Slope One and Weighted Slope One. However, a precomputing stage is needed for the mechanism. In order to receive a recommendation of the implementation in this paper, the user must at least have rated two items.
Rekommendationssystem används idag på många olika hemsidor, och är en mekanism som har syftet att, med noggrannhet, ge en personlig rekommendation av objekt till en mängd olika användare. Ett objekt kan exempelvis vara en film från Netflix. Syftet med denna rapport är att implementera en algoritm som uppfyller fem olika implementationsmål. Målen är enligt följande: algoritmen ska vara enkel att implementera, ha en effektiv tid på dataförfrågan, ge noggranna rekommendationer, sätta låga förväntningar hos användaren samt ska algoritmen inte behöva omfattande förändring vid alternering. Slope One är en förenklad version av linjär regression, och kan även användas till att rekommendera objekt. Genom att använda datamängden från Netflix Prize från 2009 och måttet Root-Mean-Square-Error (RMSE) som en utvärderare, kan Slope One generera en precision på 1.007 enheter. Den viktade Slope One, som tar hänsyn till varje föremåls relevans, genererar en precision på 0.990 enheter. När dessa två algoritmer kombineras, behövs inte större fundamentala ändringar i implementationen av Slope One. En rekommendation av något objekt kan genereras omedelbart med någon av de två algoritmerna, dock krävs det en förberäkningsfas i mekanismen. För att få en rekommendation av implementationen i denna rapport, måste användaren åtminstone ha värderat två objekt.

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7

Sun, Mingxuan. "Visualizing and modeling partial incomplete ranking data." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45793.

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Analyzing ranking data is an essential component in a wide range of important applications including web-search and recommendation systems. Rankings are difficult to visualize or model due to the computational difficulties associated with the large number of items. On the other hand, partial or incomplete rankings induce more difficulties since approaches that adapt well to typical types of rankings cannot apply generally to all types. While analyzing ranking data has a long history in statistics, construction of an efficient framework to analyze incomplete ranking data (with or without ties) is currently an open problem. This thesis addresses the problem of scalability for visualizing and modeling partial incomplete rankings. In particular, we propose a distance measure for top-k rankings with the following three properties: (1) metric, (2) emphasis on top ranks, and (3) computational efficiency. Given the distance measure, the data can be projected into a low dimensional continuous vector space via multi-dimensional scaling (MDS) for easy visualization. We further propose a non-parametric model for estimating distributions of partial incomplete rankings. For the non-parametric estimator, we use a triangular kernel that is a direct analogue of the Euclidean triangular kernel. The computational difficulties for large n are simplified using combinatorial properties and generating functions associated with symmetric groups. We show that our estimator is computational efficient for rankings of arbitrary incompleteness and tie structure. Moreover, we propose an efficient learning algorithm to construct a preference elicitation system from partial incomplete rankings, which can be used to solve the cold-start problems in ranking recommendations. The proposed approaches are examined in experiments with real search engine and movie recommendation data.

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8

Gonard, François. "Cold-start recommendation : from Algorithm Portfolios to Job Applicant Matching." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS121/document.

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La quantité d'informations, de produits et de relations potentielles dans les réseaux sociaux a rendu indispensable la mise à disposition de recommandations personnalisées. L'activité d'un utilisateur est enregistrée et utilisée par des systèmes de recommandation pour apprendre ses centres d'intérêt. Les recommandations sont également utiles lorsqu'estimer la pertinence d'un objet est complexe et repose sur l'expérience. L'apprentissage automatique offre d'excellents moyens de simuler l'expérience par l'emploi de grandes quantités de données.Cette thèse examine le démarrage à froid en recommandation, situation dans laquelle soit un tout nouvel utilisateur désire des recommandations, soit un tout nouvel objet est proposé à la recommandation. En l'absence de données d'intéraction, les recommandations reposent sur des descriptions externes. Deux problèmes de recommandation de ce type sont étudiés ici, pour lesquels des systèmes de recommandation spécialisés pour le démarrage à froid sont présentés.En optimisation, il est possible d'aborder le choix d'algorithme dans un portfolio d'algorithmes comme un problème de recommandation. Notre première contribution concerne un système à deux composants, un sélecteur et un ordonnanceur d'algorithmes, qui vise à réduire le coût de l'optimisation d'une nouvelle instance d'optimisation tout en limitant le risque d'un échec de l'optimisation. Les deux composants sont entrainés sur les données du passé afin de simuler l'expérience, et sont alternativement optimisés afin de les faire coopérer. Ce système a remporté l'Open Algorithm Selection Challenge 2017.L'appariement automatique de chercheurs d'emploi et d'offres est un problème de recommandation très suivi par les plateformes de recrutement en ligne. Une seconde contribution concerne le développement de techniques spécifiques pour la modélisation du langage naturel et leur combinaison avec des techniques de recommandation classiques afin de tirer profit à la fois des intéractions passées des utilisateurs et des descriptions textuelles des annonces. Le problème d'appariement d'offres et de chercheurs d'emploi est étudié à travers le prisme du langage naturel et de la recommandation sur deux jeux de données tirés de contextes réels. Une discussion sur la pertinence des différents systèmes de recommandations pour des applications similaires est proposée
The need for personalized recommendations is motivated by the overabundance of online information, products, social connections. This typically tackled by recommender systems (RS) that learn users interests from past recorded activities. Another context where recommendation is desirable is when estimating the relevance of an item requires complex reasoning based on experience. Machine learning techniques are good candidates to simulate experience with large amounts of data.The present thesis focuses on the cold-start context in recommendation, i.e. the situation where either a new user desires recommendations or a brand-new item is to be recommended. Since no past interaction is available, RSs have to base their reasoning on side descriptions to form recommendations. Two of such recommendation problems are investigated in this work. Recommender systems designed for the cold-start context are designed.The problem of choosing an optimization algorithm in a portfolio can be cast as a recommendation problem. We propose a two components system combining a per-instance algorithm selector and a sequential scheduler to reduce the optimization cost of a brand-new problem instance and mitigate the risk of optimization failure. Both components are trained with past data to simulate experience, and alternatively optimized to enforce their cooperation. The final system won the Open Algorithm Challenge 2017.Automatic job-applicant matching (JAM) has recently received considerable attention in the recommendation community for applications in online recruitment platforms. We develop specific natural language (NL) modeling techniques and combine them with standard recommendation procedures to leverage past user interactions and the textual descriptions of job positions. The NL and recommendation aspects of the JAM problem are studied on two real-world datasets. The appropriateness of various RSs on applications similar to the JAM problem are discussed

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9

Huang, Zan. "GRAPH-BASED ANALYSIS FOR E-COMMERCE RECOMMENDATION." Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1167%5F1%5Fm.pdf&type=application/pdf.

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10

Redpath, Jennifer Louise. "Improving the performance of recommender algorithms." Thesis, Ulster University, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.535143.

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Recommender systems were designed as a software solution to the problem of information overload. Recommendations can be generated based on the content descriptions of past purchases (Content-based), the personal ratings an individual has assigned to a set of items (Collaborative) or from a combination of both (Hybrid). There are issues that affect the performance of recommender systems, in terms of accuracy and coverage, such as data sparsity and dealing with new users and items. This thesis presents a comprehensive set of offline experiments and empirical results with the goal of improving the recommendation accuracy and coverage for the poorest performers in the dataset. This research suggests approaches for dealing with four specific research challenges: the standardisation of evaluation methods and metrics, the definition and identification of sparse users and items, improving the accuracy of hybrid systems targeted specifically at the poor performers and addressing the cold-start problem for new users. A selection of recommendation algorithms were implemented and/or extended, namely, user-based collaborative filtering, item-based collaborative filtering, collaboration-via-content and two hybrid prediction algorithms. The first two methods were developed with the express intention of providing a baseline for improvement, facilitating the identification of poor performers and analysing the factors which influenced the performance of recommendation algorithms. The later algorithms were targeted at the poor performers and were also examined with respect to user and item sparsity. The collaboration-via-content algorithm, when extended with a new content attribute, resulted in an improvement for new users. The hybrid prediction algorithms, which combined user-based and item-based approaches in such a way as to include information about transitive relationships, were able to improve upon the baseline accuracy and coverage results. In particular, the final hybrid algorithm saw a 3.5% improvement in accuracy for the poor performers compared to item-based collaborative filtering.

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11

Jedor, Matthieu. "Bandit algorithms for recommender system optimization." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM027.

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Dans cette thèse de doctorat, nous étudions l'optimisation des systèmes de recommandation dans le but de fournir des suggestions de produits plus raffinées pour un utilisateur.La tâche est modélisée à l'aide du cadre des bandits multi-bras.Dans une première partie, nous abordons deux problèmes qui se posent fréquemment dans les systèmes de recommandation : le grand nombre d'éléments à traiter et la gestion des contenus sponsorisés.Dans une deuxième partie, nous étudions les performances empiriques des algorithmes de bandit et en particulier comment paramétrer les algorithmes traditionnels pour améliorer les résultats dans les environnements stationnaires et non stationnaires qui l'on rencontre en pratique.Cela nous amène à analyser à la fois théoriquement et empiriquement l'algorithme glouton qui, dans certains cas, est plus performant que l'état de l'art
In this PhD thesis, we study the optimization of recommender systems with the objective of providing more refined suggestions of items for a user to benefit.The task is modeled using the multi-armed bandit framework.In a first part, we look upon two problems that commonly occured in recommendation systems: the large number of items to handle and the management of sponsored contents.In a second part, we investigate the empirical performance of bandit algorithms and especially how to tune conventional algorithm to improve results in stationary and non-stationary environments that arise in practice.This leads us to analyze both theoretically and empirically the greedy algorithm that, in some cases, outperforms the state-of-the-art

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12

Blot, Guillaume. "Élaboration, parcours et automatisation de traces et savoirs numériques." Thesis, Paris 4, 2017. http://www.theses.fr/2017PA040089.

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Comment l'accès au savoir peut-il être impacté par la technologie ? Il suffit d'observer le virage intenté par les outils de communication au début des années 2000 pour se rendre compte : convergence des médias, pratiques participatives et numérisation massive des données. Dans ce contexte, on imagine que l'accès au savoir tend à se démocratiser. En effet, les individus semblent se réapproprier les espaces de vie, en inversant le modèle de transmission top-down, qui va du producteur vers le consommateur, au profit de processus de transfert basés sur l'intelligence collective. Pourtant, on aurait tort de réduire cette réorganisation à un simple renversement du modèle. Car l'intelligence collective est encline à divers biais cognitifs et socio-cognitifs, amenant parfois vers des situations irrationnelles. Autrefois, on s’accommodait de ces mécaniques sociales aux conséquences limitées, aujourd'hui les savoirs numérisés constituent des ensembles massivement communiquant, donnant naissance à de nouvelles voies d'accès et à de nouveaux clivages. Pourquoi ce savoir qui n'a jamais été aussi massif et ouvert, se révèle-t-il si sélectif ? Je propose d'explorer ce paradoxe. L'enregistrement massif et constant de nos traces numériques et l'hyper-connexion des individus, participent à la construction de structures organisationnelles, où se retrouvent numérisées de manière complexe, une partie des dynamiques sociales. En formalisant de la sorte les voies navigables, ces structures organisationnelles façonnent nos trajectoires. Sur cette base, les informaticiens ont mis au point des algorithmes de parcours individualisés, ayant pour objectifs de prédire et de recommander. Ainsi, on propose d'automatiser l'accès au savoir. Se pose alors la question de la gouvernance des individus, dans un contexte où l'intelligence collective est soumise à l'infrastructure : enregistrement des traces, composition des structures organisationnelles et algorithmes de parcours
How access to knowledge can be impacted by Information Technology? In the earlier 2000s, communication tools caused a significant turn : media convergence, participative practices and massive data. In this way, free access to knowledge might tend to be democratized. People seem to regain spaces, reversing traditional top-down model, going from producer to consumer, for the benefit of an horizontal model based on collective intelligence. However, it should not automatically be assumed that this leads to a simple model reversing. Collective intelligence is subject to cognitive biases, leading to potential irrational situations. Formerly, those social mechanisms had limited consequences. Nowadays, digital knowledge are massive communicating spaces, giving birth to new access paths and new cleavages. Why this massive and open knowledge, is actually so selective? I propose to explore this paradox. Massive and constant tracking of traces and individuals hyper-connection, these two facts help organizational structures design, where social dynamics are digitalized in a complex way. These structures formalize human trajectories. On this basis, computer scientists set up prediction algorithms and recommender engines. This way, knowledge access is automatized. It can then be asked about people governance, in this context of infrastructure submission: recording traces, designing knowledge structure and automating algorithms

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13

Alkilicgil, Erdem. "User Modeling In Mobile Environment." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606852/index.pdf.

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The popularity of e-commerce sites and applications that use recommendations and user modeling is increased recently. The development and contest in tourism calls attention of large-scale IT companies. These companies have started to work on recommendation systems and user modeling on tourism sector. Some of the clustering methodologies, neighboring methods and machine learning algorithms are commenced to use for making predictions about tourist&rsquo
s interests while he/she is traveling around the city. Recommendation ability is the most interesting thing for a tourist guide application. Recommender systems are composed of two main approaches, collaborative and content-based filtering. Collaborative filtering algorithms look for people that have similar interests and properties, while contentbased filtering methods pay attention to sole user&rsquo
s interests and properties to make recommendations. Both of the approaches have advantages and disadvantages, for that reason sometimes these two approaches are used together. Chosen method directly affects the recommendation quality, so advantages and disadvantages of both methods will be examined carefully. Recommendation of locations or services can be seen as a classification problem. Artificial intelligent systems like neural networks, genetic algorithms, particle swarm optimization algorithms, artificial immune systems are inspired from natural life and can be used as classifier systems. Artificial immune system, inspired from human immune system, has ability to classify huge numbers of different patterns. In this paper ESGuide, a tourist guide application that uses artificial immune system is examined. ESGuide application is a client-server application that helps tourists while they are traveling around the city. ESGuide has two components: Map agent and recommender agent. Map agent helps the tourist while he/she interacts with the city map. Tourist should rate the locations and items while traveling. Due to these ratings and client-server interaction, recommender agent tries to predict user interested places and items. Tourist has a chance to state if he/she likes the recommendation or not. If the tourist does not like the recommendation, new recommendation set is created and presented to the user.

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14

Tso-Sutter, Karen H. L. "Towards metadata-aware algorithms for recommender systems." Frankfurt, M. Berlin Bern Bruxelles New York, NY Oxford Wien Lang, 2008. http://d-nb.info/999133063/04.

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15

Li, Lei. "Next Generation of Recommender Systems: Algorithms and Applications." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1446.

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Personalized recommender systems aim to assist users in retrieving and accessing interesting items by automatically acquiring user preferences from the historical data and matching items with the preferences. In the last decade, recommendation services have gained great attention due to the problem of information overload. However, despite recent advances of personalization techniques, several critical issues in modern recommender systems have not been well studied. These issues include: (1) understanding the accessing patterns of users (i.e., how to effectively model users' accessing behaviors); (2) understanding the relations between users and other objects (i.e., how to comprehensively assess the complex correlations between users and entities in recommender systems); and (3) understanding the interest change of users (i.e., how to adaptively capture users' preference drift over time). To meet the needs of users in modern recommender systems, it is imperative to provide solutions to address the aforementioned issues and apply the solutions to real-world applications. The major goal of this dissertation is to provide integrated recommendation approaches to tackle the challenges of the current generation of recommender systems. In particular, three user-oriented aspects of recommendation techniques were studied, including understanding accessing patterns, understanding complex relations and understanding temporal dynamics. To this end, we made three research contributions. First, we presented various personalized user profiling algorithms to capture click behaviors of users from both coarse- and fine-grained granularities; second, we proposed graph-based recommendation models to describe the complex correlations in a recommender system; third, we studied temporal recommendation approaches in order to capture the preference changes of users, by considering both long-term and short-term user profiles. In addition, a versatile recommendation framework was proposed, in which the proposed recommendation techniques were seamlessly integrated. Different evaluation criteria were implemented in this framework for evaluating recommendation techniques in real-world recommendation applications. In summary, the frequent changes of user interests and item repository lead to a series of user-centric challenges that are not well addressed in the current generation of recommender systems. My work proposed reasonable solutions to these challenges and provided insights on how to address these challenges using a simple yet effective recommendation framework.

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16

Anne, Patricia Anne. "Semantically and Contextually-Enhanced Collaborative Filtering Recommender Algorithms." Thesis, University of Ulster, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.516289.

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17

Ghazanfar, Mustansar Ali. "Robust, scalable, and practical algorithms for recommender systems." Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/343761/.

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The purpose of recommender systems is to filter information unseen by a user to predict whether a user would like a given item. Making effective recommendations from a domain consisting of millions of ratings is a major research challenge in the application of machine learning and data mining. A number of approaches have been proposed to solvethe recommendation problem, where the main motivation is to increase the accuracy of the recommendations while ignoring other design objectives such as scalability, sparsity and imbalanced dataset problems, cold-start problems, and long tail problems. The aim of this thesis is to develop recommendation algorithms that satisfy the aforementioned design objectives making the recommendation generation techniques applicable to a wider range of practical situations and real-world scenarios. With this in mind, in the first half of the thesis, we propose novel hybrid recommendation algorithms that give accurate results and eliminate some of the known problems with recommender systems. More specifically, we propose a novel switching hybrid recommendation framework that combines Collaborative Filtering (CF) with a content-based filtering algorithm. Our experiments show that the performance of our algorithm is better than (or comparable to) the other hybrid recommendation approaches available in the literature. While reducing the dimensions of the dataset by Singular Value Decomposition (SVD), prior to applying CF, we discover that the SVD-based CF fails to produce reliable recommendations for some datasets. After further investigation, we fi�nd out that the SVD-based recommendations depend on the imputation methods used to approximate the missing values in the user-item rating matrix. We propose various missing value imputation methods, which exhibit much superior accuracy and performance compared to the traditional missing value imputation method - item average. Furthermore, we show how the gray-sheep users problem associated with a recommender systemcan effectively be solved using the K-means clustering algorithm. After analysing the effect of different centroid selection approaches and distance measures in the K-means clustering algorithm, we demonstrate how the gray-sheep users in a recommender system can be identified by treating them as an outlier problem. We demonstrate that the performance (accuracy and coverage) of the CF-based algorithms suffers in the case of gray-sheep users. We propose a hybrid recommendation algorithm to solve the gray-sheep users problem. In the second half of the thesis, we propose a new class of kernel mapping recommender system methods that we call KMR for solving the recommendation problem. The proposed methods find the multi-linear mapping between two vector spaces based on the structure-learning technique. We propose the user- and item-based versions of the KMR algorithms and offer various ways to combine them. We report results of an extensive evaluation conducted on five different datasets under various recommendation conditions. Our empirical study shows that the proposed algorithms offer a state-of-the-art performance and provide robust performance under all conditions. Furthermore, our algorithms are quite flexible as they can incorporate more information|ratings, demographics, features, and contextual information|easily into the forms of kernels and moreover, these kernels can be added/multiplied. We then adapt the KMR algorithm to incorporate new data incrementally. We offer a new heuristic namely KMRincr that can build the model without retraining the whole model from scratch when new data are added to the recommender system, providing significant computation savings. Our final contribution involves adapting the KMR algorithms to build the model on-line. More specifically, we propose a perceptron-type algorithm namely KMR percept which is a novel, fast, on-line algorithm for building the model that maintains good accuracy and scales well with the data. We provide the temporal analysis of the KMR percept algorithm. The empirical results reveal that the performance of the KMR percept is comparable to the KMR, and furthermore, it overcomes some of the conventional problems with recommender systems.

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18

Ribeiro, Marco Tulio Correia. "Multi-objective pareto-efficient algorithms for recommender systems." Universidade Federal de Minas Gerais, 2013. http://hdl.handle.net/1843/ESSA-9CHG5H.

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Recommender systems are quickly becoming ubiquitous in applications such as ecommerce, social media channels and content providers, acting as enabling mechanisms designed to overcome the information overload problem by improving browsing and consumption experience. A typical task in recommender systems is to output a ranked list of items, so that items placed higher in the rank are more likely to be interesting to the users. Interestingness measures include how accurate, novel and diverse the suggested items are, and the objective is usually to produce ranked lists optimizing one of these measures. Suggesting items that are simultaneously accurate, novel and diverse is much more challenging, since this may lead to a conflicting-objective problem, in which the attempt to improve a measure further may result in worsening other measures. In this thesis we propose new approaches for multi-objective recommender systems based on the concept of Pareto-efficiency -- a state achieved when the system is devised in the most efficient manner in the sense that there is no way to improve one of the objectives without making any other objective worse off. Given that existing recommendation algorithms differ in their level of accuracy, diversity and novelty, we exploit the Pareto-efficiency concept in two distinct manners: (i) the aggregation of ranked lists produced by existing algorithms into a single one, which we call Paretoefficient ranking, and (ii) the weighted combination of existing algorithms resulting in a hybrid one, which we call Pareto-efficient hybridization. Our evaluation involves two real application scenarios: music recommendation with implicit feedback (i.e., Last.fm) and movie recommendation with explicit feedback (i.e., MovieLens). We show that the proposed approaches are effective in optimizing each of the metrics without hurting the others, or optimizing all three simultaneously. Further, for the Pareto-efficient hybridization, we allow for adjusting the compromise between the metrics, so that the recommendation emphasis can be set dinamically according to the needs of different users.
Sistemas de recomendação tem se tornado cada vez mais populares em aplicações como e-commerce, mídias sociais e provedores de conteúdo. Esses sistemas agem como mecanismos para lidar com o problema da sobrecarga de informação. Uma tarefa comum em sistemas de recomendação é a de ordenar um conjunto de itens, de forma que os itens no topo da lista sejam de interesse para os usuários. O conceito de interesse pode ser medido observando a acurácia, novidade e diversidade dos itens sugeridos. Geralmente, o objetivo de um sistema de recomendação é gerar listas ordenadas de forma a otimizar uma dessas métricas. Um problema mais difícil é tentar otimizar as três métricas (ou objetivos) simultaneamente, o que pode levar ao caso onde a tentativa de melhorar em uma das métricas pode piorar o resultado nas outras métricas. Neste trabalho, propomos novas abordagens para sistemas de recomendaççao multi-objetivo, baseadas no conceito de Eficiência de Pareto -- um estado obtido quando o sistema é de tal forma que não há como melhorar em algum objetivo sem piorar em outro objetivo. Dado que os algoritmos de recomendação existentes diferem em termos de acurácia, diversidade e novidade, exploramos o conceito de Eficiência de Pareto de duas formas distintas: (i) agregando listas ordenadas produzidas por algoritmos existentes de forma a obter uma lista única - abordagem que chamamos de ranking Pareto-eficiente, e (ii), a combinação linear ponderada de algoritmos existentes, resultado em um híbrido, abordagem que chamamos de hibridização Pareto-eficiente. Nossa avaliação envolve duas aplicações reais: recomendação de música com feedback implícito (i.e., Last.fm) e recomendação de filmes com feedback explícito (i.e., Movielens). Nós mostramos que as abordagens Pareto-eficientes são efetivas em recomendar items com bons niveis de acurácia, novidade e diversidade (simultaneamente), ou uma das métricas sem piorar as outras. Além disso, para a hibridização Pareto-eficiente, provemos uma forma de ajustar o compromisso entre acurácia, novidade e diversidade, de forma que a ênfase da recomendação possa ser ajustada dinamicamente para usuários diferentes.

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19

Ozbal, Gozde. "A Content Boosted Collaborative Filtering Approach For Movie Recommendation Based On Local &amp." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610984/index.pdf.

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Recently, it has become more and more difficult for the existing web based systems to locate or retrieve any kind of relevant information, due to the rapid growth of the World Wide Web (WWW) in terms of the information space and the amount of the users in that space. However, in today'
s world, many systems and approaches make it possible for the users to be guided by the recommendations that they provide about new items such as articles, news, books, music, and movies. However, a lot of traditional recommender systems result in failure when the data to be used throughout the recommendation process is sparse. In another sense, when there exists an inadequate number of items or users in the system, unsuccessful recommendations are produced. Within this thesis work, ReMovender, a web based movie recommendation system, which uses a content boosted collaborative filtering approach, will be presented. ReMovender combines the local/global similarity and missing data prediction v techniques in order to handle the previously mentioned sparseness problem effectively. Besides, by putting the content information of the movies into consideration during the item similarity calculations, the goal of making more successful and realistic predictions is achieved.

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20

Salam, Patrous Ziad, and Safir Najafi. "Evaluating Prediction Accuracy for Collaborative Filtering Algorithms in Recommender Systems." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186456.

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Recommender systems are a relatively new technology that is commonly used by e-commerce websites and streaming services among others, to predict user opinion about products. This report studies two specific recommender algorithms, namely FunkSVD, a matrix factorization algorithm and Item-based collaborative filtering, which utilizes item similarity. This study aims to compare the prediction accuracy of the algorithms when ran on a small and a large dataset. By performing cross-validation on the algorithms, this paper seeks to obtain data that supposedly may clarify ambiguities regarding the accuracy of the algorithms. The tests yielded results which indicated that the FunkSVD algorithm may be more accurate than the Item-based collaborative filtering algorithm, but further research is required to come to a concrete conclusion.

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21

Ayday, Erman. "Iterative algorithms for trust and reputation management and recommender systems." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/45868.

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This thesis investigates both theoretical and practical aspects of the design and analysis of iterative algorithms for trust and reputation management and recommender systems. It also studies the application of iterative trust and reputation management mechanisms in ad-hoc networks and P2P systems. First, an algebraic and iterative trust and reputation management scheme (ITRM) is proposed. The proposed ITRM can be applied to centralized schemes, in which a central authority collects the reports and forms the reputations of the service providers (sellers) as well as report/rating trustworthiness of the (service) consumers (buyers). It is shown that ITRM is robust in filtering out the peers who provide unreliable ratings. Next, the first application of Belief Propagation algorithm, a fully iterative probabilistic algorithm, on trust and reputation management (BP-ITRM) is proposed. In BP-ITRM, the reputation management problem is formulated as an inference problem, and it is described as computing marginal likelihood distributions from complicated global functions of many variables. However, it is observed that computing the marginal probability functions is computationally prohibitive for large scale reputation systems. Therefore, the belief propagation algorithm is utilized to efficiently (in linear complexity) compute these marginal probability distributions. In BP-ITRM, the reputation system is modeled by using a factor graph and reputation values of the service providers (sellers) are computed by iterative probabilistic message passing between the factor and variable nodes on the graph. It is shown that BP-ITRM is reliable in filtering out malicious/unreliable reports. It is proven that BP-ITRM iteratively reduces the error in the reputation values of service providers due to the malicious raters with a high probability. Further, comparison of BP-ITRM with some well-known and commonly used reputation management techniques (e.g., Averaging Scheme, Bayesian Approach and Cluster Filtering) indicates the superiority of the proposed scheme both in terms of robustness against attacks and efficiency. The introduction of the belief propagation and iterative message passing methods onto trust and reputation management has opened up several research directions. Thus, next, the first application of the belief propagation algorithm in the design of recommender systems (BPRS) is proposed. In BPRS, recommendations (predicted ratings) for each active user are iteratively computed by probabilistic message passing between variable and factor nodes in a factor graph. It is shown that as opposed to the previous recommender algorithms, BPRS does not require solving the recommendation problem for all users if it wishes to update the recommendations for only a single active user using the most recent data (ratings). Further, BPRS computes the recommendations for each user with linear complexity, without requiring a training period while it remains comparable to the state of art methods such as Correlation-based neighborhood model (CorNgbr) and Singular Value Decomposition (SVD) in terms of rating and precision accuracy. This work also explores fundamental research problems related to application of iterative and probabilistic reputation management systems in various fields (such as ad-hoc networks and P2P systems). A distributed malicious node detection mechanism is proposed for delay tolerant networks (DTNs) using ITRM which enables every node to evaluate other nodes based on their past behavior, without requiring a central authority. Further, for the first time. the belief propagation algorithm is utilized in the design and evaluation of distributed trust and reputation management systems for P2P networks. Several schemes are extensively simulated and are compared to demonstrate the effectiveness of the iterative algorithms and belief propagation on these applications.

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22

Tabari, Michel, and Rawand Sultani. "A comparison of matrix factorization algorithms for a movie recommender system." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229734.

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Recommendation systems is a growing technique for providing a better user experience for discovering new content on a platform. It can be implemented in many contexts such as Netflix for recommending movies. There are many ways to implement recommendation systems. This paper investigated two of these methods - Weighted Alternating Least Squares and Stochastic Gradient Descent - which fall into the category of matrix factorization and measured their performance in regards to time taken for training, error convergence and prediction quality. To our help we have used TensorFlow, a machine learning framework developed by Google which have been providing us with algorithms, models for training, and testing. The results showed that the Weighted Alternating Least Squares model proved to be better in terms of prediction quality: We also found that the quality of our predictions relied heavily on the model's parameters, since optimal predictions for a model can be found through the correct tuning. We concluded that the choice of model depends heavily on the data set investigated, and that optimal parameters for one model cannot simply be transferred to another model.
Rekommendationssystem används alltmer för att förbättra användarupplevelser. Dessa kan implementeras i många sammanhang som i streamingplattformen Netflix för att rekommendera filmer till sina användare. Det finns många sätt att implementera rekommendationssystem och i denna rapport undersöktes två av dessa metoder - Weighted Alternating Least Squares och Stochastic Gradient Descent - som ligger inom kategorin av matrisfaktorisering och deras diverse prestandamått som träningstid, felkonvergens samt kvalitén på förslagen. Till vår hjälp användes TensorFlow, ett ramverk för maskininlärning som utvecklats av Google som har tillhandahållit oss modeller och algoritmer. Resultatet var att Weighted Alternating Least Squares modellen visade sig vara bättre med avseende på kvalitén på förslagen och vi fann även att kvalitén var starkt beroende av modellens parametrar, då vi fann att optimala förslag för en modell kan hittas genom korrekt justering av dessa parametrar. Vi drog slutsatsen att valet av modell beror på den data som undersöks och att optimala parametrar för en modell inte direkt kan överföras till en annan.

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23

Paraschakis, Dimitris. "Algorithmic and Ethical Aspects of Recommender Systems in e-Commerce." Licentiate thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-7792.

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Recommender systems have become an integral part of virtually every e-commerce application on the web. These systems enable users to quickly discover relevant products, at the same time increasing business value. Over the past decades, recommender systems have been modeled using numerous machine learning techniques. However, the adoptability of these models by commercial applications remains unclear. We assess the receptiveness of the industrial sector to algorithmic contributions of the research community by surveying more than 30 e-commerce platforms, and experimenting with various recommenders on proprietary e-commerce datasets. Another overlooked but important factor that complicates the design and use of recommender systems is their ethical implications. We identify and summarize these issues in our ethical recommendation framework, which also enables users to control sensitive moral aspects of recommendations via the proposed “ethical toolbox”. The feasibility of this tool is supported by the results of our user study. Because of moral implications associated with user profiling, we investigate algorithms capable of generating user-agnostic recommendations. We propose an ensemble learning scheme based on Thompson Sampling bandit policy, which models arms as base recommendation functions. We show how to adapt this algorithm to realistic situations when neither arm availability nor reward stationarity is guaranteed.

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24

Jess, Torben. "Recommender systems and market approaches for industrial data management." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/270103.

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Industrial companies are dealing with an increasing data overload problem in all aspects of their business: vast amounts of data are generated in and outside each company. Determining which data is relevant and how to get it to the right users is becoming increasingly difficult. There are a large number of datasets to be considered, and an even higher number of combinations of datasets that each user could be using. Current techniques to address this data overload problem necessitate detailed analysis. These techniques have limited scalability due to their manual effort and their complexity, which makes them unpractical for a large number of datasets. Search, the alternative used by many users, is limited by the user’s knowledge about the available data and does not consider the relevance or costs of providing these datasets. Recommender systems and so-called market approaches have previously been used to solve this type of resource allocation problem, as shown for example in allocation of equipment for production processes in manufacturing or for spare part supplier selection. They can therefore also be seen as a potential application for the problem of data overload. This thesis introduces the so-called RecorDa approach: an architecture using market approaches and recommender systems on their own or by combining them into one system. Its purpose is to identify which data is more relevant for a user’s decision and improve allocation of relevant data to users. Using a combination of case studies and experiments, this thesis develops and tests the approach. It further compares RecorDa to search and other mechanisms. The results indicate that RecorDa can provide significant benefit to users with easier and more flexible access to relevant datasets compared to other techniques, such as search in these databases. It is able to provide a fast increase in precision and recall of relevant datasets while still keeping high novelty and coverage of a large variety of datasets.

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25

Makari, Manshadi Faraz [Verfasser], and Rainer [Akademischer Betreuer] Gemulla. "Scalable optimization algorithms for recommender systems / Faraz Makari Manshadi. Betreuer: Rainer Gemulla." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2014. http://d-nb.info/1062947630/34.

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26

Makari, Manshadi Faraz Verfasser], and Rainer [Akademischer Betreuer] [Gemulla. "Scalable optimization algorithms for recommender systems / Faraz Makari Manshadi. Betreuer: Rainer Gemulla." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:291-scidok-59221.

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27

Yao, Sirui. "Evaluating, Understanding, and Mitigating Unfairness in Recommender Systems." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103779.

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Recommender systems are information filtering tools that discover potential matchings between users and items and benefit both parties. This benefit can be considered a social resource that should be equitably allocated across users and items, especially in critical domains such as education and employment. Biases and unfairness in recommendations raise both ethical and legal concerns. In this dissertation, we investigate the concept of unfairness in the context of recommender systems. In particular, we study appropriate unfairness evaluation metrics, examine the relation between bias in recommender models and inequality in the underlying population, as well as propose effective unfairness mitigation approaches. We start with exploring the implication of fairness in recommendation and formulating unfairness evaluation metrics. We focus on the task of rating prediction. We identify the insufficiency of demographic parity for scenarios where the target variable is justifiably dependent on demographic features. Then we propose an alternative set of unfairness metrics that measured based on how much the average predicted ratings deviate from average true ratings. We also reduce these unfairness in matrix factorization (MF) models by explicitly adding them as penalty terms to learning objectives. Next, we target a form of unfairness in matrix factorization models observed as disparate model performance across user groups. We identify four types of biases in the training data that contribute to higher subpopulation error. Then we propose personalized regularization learning (PRL), which learns personalized regularization parameters that directly address the data biases. PRL poses the hyperparameter search problem as a secondary learning task. It enables back-propagation to learn the personalized regularization parameters by leveraging the closed-form solutions of alternating least squares (ALS) to solve MF. Furthermore, the learned parameters are interpretable and provide insights into how fairness is improved. Third, we conduct theoretical analysis on the long-term dynamics of inequality in the underlying population, in terms of the fitting between users and items. We view the task of recommendation as solving a set of classification problems through threshold policies. We mathematically formulate the transition dynamics of user-item fit in one step of recommendation. Then we prove that a system with the formulated dynamics always has at least one equilibrium, and we provide sufficient conditions for the equilibrium to be unique. We also show that, depending on the item category relationships and the recommendation policies, recommendations in one item category can reshape the user-item fit in another item category. To summarize, in this research, we examine different fairness criteria in rating prediction and recommendation, study the dynamic of interactions between recommender systems and users, and propose mitigation methods to promote fairness and equality.
Doctor of Philosophy
Recommender systems are information filtering tools that discover potential matching between users and items. However, a recommender system, if not properly built, may not treat users and items equitably, which raises ethical and legal concerns. In this research, we explore the implication of fairness in the context of recommender systems, study the relation between unfairness in recommender output and inequality in the underlying population, and propose effective unfairness mitigation approaches. We start with finding unfairness metrics appropriate for recommender systems. We focus on the task of rating prediction, which is a crucial step in recommender systems. We propose a set of unfairness metrics measured as the disparity in how much predictions deviate from the ground truth ratings. We also offer a mitigation method to reduce these forms of unfairness in matrix factorization models Next, we look deeper into the factors that contribute to error-based unfairness in matrix factorization models and identify four types of biases that contribute to higher subpopulation error. Then we propose personalized regularization learning (PRL), which is a mitigation strategy that learns personalized regularization parameters to directly addresses data biases. The learned per-user regularization parameters are interpretable and provide insight into how fairness is improved. Third, we conduct a theoretical study on the long-term dynamics of the inequality in the fitting (e.g., interest, qualification, etc.) between users and items. We first mathematically formulate the transition dynamics of user-item fit in one step of recommendation. Then we discuss the existence and uniqueness of system equilibrium as the one-step dynamics repeat. We also show that depending on the relation between item categories and the recommendation policies (unconstrained or fair), recommendations in one item category can reshape the user-item fit in another item category. In summary, we examine different fairness criteria in rating prediction and recommendation, study the dynamics of interactions between recommender systems and users, and propose mitigation methods to promote fairness and equality.

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28

Huttner, Joseph. "From Tapestry to SVD a survey of the algorithms that power Recommender systems /." Diss., Connect to the thesis, 2009. http://hdl.handle.net/10066/3706.

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29

Williams, Alyssa. "Hybrid Recommender Systems via Spectral Learning and a Random Forest." Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/etd/3666.

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We demonstrate spectral learning can be combined with a random forest classifier to produce a hybrid recommender system capable of incorporating meta information. Spectral learning is supervised learning in which data is in the form of one or more networks. Responses are predicted from features obtained from the eigenvector decomposition of matrix representations of the networks. Spectral learning is based on the highest weight eigenvectors of natural Markov chain representations. A random forest is an ensemble technique for supervised learning whose internal predictive model can be interpreted as a nearest neighbor network. A hybrid recommender can be constructed by first deriving a network model from a recommender's similarity matrix then applying spectral learning techniques to produce a new network model. The response learned by the new version of the recommender can be meta information. This leads to a system capable of incorporating meta data into recommendations.

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30

Haglund, Isac, and Lisa Johansson. "A comparative study of algorithms used in recommender systems : measuring their accuracy on cold start data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280327.

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A common problem today is the increasing amount of information that people get exposed to, an issue that recommender systems aim to fix through giving personalized recommendations. The most widely used technique within recommender systems is collaborative filtering, which uses the similarity between users who share the same interest. A recommendation can, therefore, be done by finding users with similar interests and finding new items through those. A known problem within recommender systems is the cold-start problem. This arises when a new user or a new item is added to the system. Due to limited information about those, it becomes more difficult to generate accurate personalized recommendations. The aim of this report is to study how a set of algorithms within collaborative filtering performs during the cold-start problem. The chosen algorithms are SVD, SVD++, and Slope One. Both SVD and SVD++ belong to a model- based approach, and Slope One belongs to a memory-based approach, two categories that algorithms within collaborative filtering are divided into. The result of the study indicates that the memory-based algorithm, Slope One, is less accurate and has lower performance than the model-based algorithms, SVD and SVD++, which is in line with previous research. Regarding SVD and SVD++, further studies need to be conducted in order to conclude which of them performs the best during the cold-start problem.
Ett vanligt problem idag är den ökande mängd information som personer blir exponerad för, ett problem som rekommendationssystem syftar till att lösa genom att ge personliga rekommendationer. Den mest använda modellen inom rekommendationssystem är collaborative filtering, som använder likheter mellan användare som delar samma intresse. En rekommendation kan därför göras genom att hitta användare med liknade intresse och hitta nya produkter via den. Ett känt problem inom rekommendationssystem är cold-start problemet. Detta uppstår när en ny användare eller en ny produkt läggs till i systemet. På grund av begränsad information om dessa blir det svårare att generera korrekta personliga rekommendationer. Syftet med den här rapporten är att studera hur en mängd algoritmer inom collaborative filtering presterar under cold-start problemet. Dem valda algoritmerna är SVD, SVD++ och Slope One. Både SVD och SVD++ är modellbaserade, medan Slope One är minnesbaserad. Dessa är två kategorier som algoritmerna inom collaborative filtering delas in i. Resultatet av studien indikerar på att den minnesbaserade algoritmen Slope One har sämre prestanda än dem modellbaserade algoritmerna SVDoch SVD++, vilket är i enlighet med tidigare forskningar. Vad gäller SVD och SVD++ behöver ytterligare studier genomföras för att kunna dra någon slutsats om vilken av algoritmerna som presterar bäst under cold-start problemet.

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31

Svebrant, Henrik, and John Svanberg. "A comparative study of the conventional item-based collaborative filtering and the Slope One algorithms for recommender systems." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186449.

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Recommender systems are an important research topic in todays society as the amount of data increases across the globe. In order for commercial systems to give their users good and personalized recommendations on what data may be of interest to them in an effective manner, such a system must be able to give recommendations quickly and scale well as data increases. The purpose of this study is to evaluate two such algorithms with this in mind. The two different algorithm families tested are classified as item-based collaborative filtering but work very differently. It is therefore of interest to see how their complexities affect their performance, accuracy as well as scalability. The Slope One family is much simpler to implement and proves to be equally as efficient, if not even more efficient than the conventional item-based ones. Both families do require a precomputation stage before recommendations are possible to give, this is the stage where Slope One suffers in comparison to the conventional item-based one. The algorithms are tested using Lenskit, on data provided by GroupLens and their MovieLens project.

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32

Buzzoni, Marco. "Definizione di un sistema di raccomandazione basato su reti commerciali." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/6208/.

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Nel contesto economico odierno i sistemi di raccomandazione rappresentano uno strumento utile al fine di aumentare le vendite con pubblicità e promozioni su misura per ciascun utente. Tali strumenti trovano numerose applicazioni nei siti di e-commerce, si pensi ad Amazon o a MovieLens. Esistono diverse tipologie di sistemi di raccomandazione, che si differenziano principalmente per il modo con cui sono prodotte le raccomandazioni per gli utenti. In questa tesi se ne vuole definire una nuova tipologia, che superi la restrizione del vincolo ad un sito a ad una società, fornendo agli utenti raccomandazioni di prodotti acquistabili in negozi reali e il più possibile accessibili, nel senso geografico del termine. Si e inoltre astratto il concetto di raccomandazione, passando da un insieme omogeneo di oggetti ad un insieme eterogeneo di entità ottenibili attraverso lo svolgimento di attività. Con queste premesse il sistema da definire dovrà raccomandare non più solo entità, ma entità e shop presso i quali sono disponibili per le persone.

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Bouneffouf, Djallel. "DRARS, A Dynamic Risk-Aware Recommender System." Phd thesis, Institut National des Télécommunications, 2013. http://tel.archives-ouvertes.fr/tel-01026136.

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L'immense quantité d'information générée et gérée au quotidien par les systèmes d'information et leurs utilisateurs conduit inéluctablement ?a la problématique de surcharge d'information. Dans ce contexte, les systèmes de recommandation traditionnels fournissent des informations pertinentes aux utilisateurs. Néanmoins, avec la propagation récente des dispositifs mobiles (Smartphones et tablettes), nous constatons une migration progressive des utilisateurs vers la manipulation d'environnements pérvasifs. Le problème avec les approches traditionnelles de recommandation est qu'elles n'utilisent pas toute l'information disponible pour produire des recommandations. Davantage d'informations contextuelles pourraient être utilisées dans le processus de recommandation pour aboutir à des recommandations plus précises. Les systèmes de recommandations sensibles au contexte (CARS) combinent les caractéristiques des systèmes sensibles au contexte et des systèmes de recommandation an de fournir des informations personnalisées aux utilisateurs dans des environnements ubiquitaires. Dans cette perspective ou tout ce qui concerne l'utilisateur est dynamique, les contenus qu'il manipule et son environnement, deux questions principales doivent être adressées : i) Comment prendre en compte la dynamicité des contenus de l'utilisateur ? et ii ) Comment éviter d'être intrusif en particulier dans des situations critiques ?. En réponse ?a ces questions, nous avons développé un système de recommandation dynamique et sensible au risque appelé DRARS (Dynamic Risk-Aware Recommender System), qui modélise la recommandation sensible au contexte comme un problème de bandit. Ce système combine une technique de filtrage basée sur le contenu et un algorithme de bandit contextuel. Nous avons montré que DRARS améliore la stratégie de l'algorithme UCB (Upper Con dence Bound), le meilleur algorithme actuellement disponible, en calculant la valeur d'exploration la plus optimale pour maintenir un compromis entre exploration et exploitation basé sur le niveau de risque de la situation courante de l'utilisateur. Nous avons mené des expériences dans un contexte industriel avec des données réelles et des utilisateurs réels et nous avons montré que la prise en compte du niveau de risque de la situation de l'utilisateur augmentait significativement la performance du système de recommandation.

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Li, Siying. "Context-aware recommender system for system of information systems." Thesis, Compiègne, 2021. http://www.theses.fr/2021COMP2602.

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Travailler en collaboration n’est plus une question mais une réalité, la question qui se pose aujourd’hui concerne la mise en œuvre de la collaboration de façon à ce qu’elle soit la plus réussie possible. Cependant, une collaboration réussie n’est pas facile et est conditionnée par différents facteurs qui peuvent l’influencer. Il est donc nécessaire de considérer ces facteurs au sein du contexte de collaboration pour favoriser l’efficacité de collaboration. Parmi ces facteurs, le collaborateur est un facteur principal, qui est étroitement associé à l’efficacité et à la réussite des collaborations. Le choix des collaborateurs et/ou la recommandation de ces derniers en tenant compte du contexte de la collaboration peut grandement influencer la réussite de cette dernière. En même temps, grâce au développement des technologies de l’information, de nombreux outils numériques de collaboration sont mis à la disposition tels que les outils de mail et de chat en temps réel. Ces outils numériques peuvent eux-mêmes être intégrés dans un environnement de travail collaboratif basé sur le web. De tels environnements permettent aux utilisateurs de collaborer au-delà de la limite des distances géographiques. Ces derniers laissent ainsi des traces d’activités qu’il devient possible d’exploiter. Cette exploitation sera d’autant plus précise que le contexte sera décrit et donc les traces enregistrées riches en description. Il devient donc intéressant de développer les environnements de travail collaboratif basé sur le web en tenant d’une modélisation du contexte de la collaboration. L’exploitation des traces enregistrés pourra alors prendre la forme de recommandation contextuelle de collaborateurs pouvant renforcer la collaboration. Afin de générer des recommandations de collaborateurs dans des environnements de travail collaboratifs basés sur le web, cette thèse se concentre sur la génération des recommandations contextuelles de collaborateurs en définissant, modélisant et traitant le contexte de collaboration. Pour cela, nous proposons d’abord une définition du contexte de collaboration et choisissons de créer une ontologie du contexte de collaboration compte tenu des avantages de l’approche de modélisation en l’ontologie. Ensuite, une similarité sémantique basée sur l’ontologie est développée et appliquée dans trois algorithmes différents (i.e., PreF1, PoF1 et PoF2) afin de générer des recommandations contextuelles des collaborateurs. Par ailleurs, nous déployons l’ontologie de contexte de collaboration dans des environnements de travail collaboratif basés sur le web en considérant une architecture de système des systèmes d’informations du point de vue des environnements de travail collaboratif basés sur le web. À partir de cette architecture, un prototype correspondant d’environnement de travail collaboratif basé sur le web est alors construit. Enfin, un ensemble de données de collaborations scientifiques est utilisé pour tester et évaluer les performances des trois algorithmes de recommandation contextuelle des collaborateurs
Working collaboratively is no longer an issue but a reality, what matters today is how to implement collaboration so that it is as successful as possible. However, successful collaboration is not easy and is conditioned by different factors that can influence it. It is therefore necessary to take these impacting factors into account within the context of collaboration for promoting the effectiveness of collaboration. Among the impacting factors, collaborator is a main one, which is closely associated with the effectiveness and success of collaborations. The selection and/or recommendation of collaborators, taking into account the context of collaboration, can greatly influence the success of collaboration. Meanwhile, thanks to the development of information technology, many collaborative tools are available, such as e-mail and real-time chat tools. These tools can be integrated into a web-based collaborative work environment. Such environments allow users to collaborate beyond the limit of geographical distances. During collaboration, users can utilize multiple integrated tools, perform various activities, and thus leave traces of activities that can be exploited. This exploitation will be more precise when the context of collaboration is described. It is therefore worth developing web-based collaborative work environments with a model of the collaboration context. Processing the recorded traces can then lead to context-aware collaborator recommendations that can reinforce the collaboration. To generate collaborator recommendations in web-based Collaborative Working Environments, this thesis focuses on producing context-aware collaborator recommendations by defining, modeling, and processing the collaboration context. To achieve this, we first propose a definition of the collaboration context and choose to build a collaboration context ontology given the advantages of the ontology-based modeling approach. Next, an ontologybased semantic similarity is developed and applied in three different algorithms (i.e., PreF1, PoF1, and PoF2) to generate context-aware collaborator recommendations. Furthermore, we deploy the collaboration context ontology into web-based Collaborative Working Environments by considering an architecture of System of Information Systems from the viewpoint of web-based Collaborative Working Environments. Based on this architecture, a corresponding prototype of web-based Collaborative Working Environment is then constructed. Finally, a dataset of scientific collaborations is employed to test and evaluate the performances of the three context-aware collaborator recommendation algorithms

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Holländer, John. "Investigating the performance of matrix factorization techniques applied on purchase data for recommendation purposes." Thesis, Malmö högskola, Fakulteten för teknik och samhälle (TS), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20624.

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Automated systems for producing product recommendations to users is a relatively new area within the field of machine learning. Matrix factorization techniques have been studied to a large extent on data consisting of explicit feedback such as ratings, but to a lesser extent on implicit feedback data consisting of for example purchases.The aim of this study is to investigate how well matrix factorization techniques perform compared to other techniques when used for producing recommendations based on purchase data. We conducted experiments on data from an online bookstore as well as an online fashion store, by running algorithms processing the data and using evaluation metrics to compare the results. We present results proving that for many types of implicit feedback data, matrix factorization techniques are inferior to various neighborhood- and association rules techniques for producing product recommendations. We also present a variant of a user-based neighborhood recommender system algorithm \textit{(UserNN)}, which in all tests we ran outperformed both the matrix factorization algorithms and the k-nearest neighbors algorithm regarding both accuracy and speed. Depending on what dataset was used, the UserNN achieved a precision approximately 2-22 percentage points higher than those of the matrix factorization algorithms, and 2 percentage points higher than the k-nearest neighbors algorithm. The UserNN also outperformed the other algorithms regarding speed, with time consumptions 3.5-5 less than those of the k-nearest neighbors algorithm, and several orders of magnitude less than those of the matrix factorization algorithms.

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Bouayad, Lina. "Analytics and Healthcare Costs (A Three Essay Dissertation)." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5876.

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Both literature and practice have looked at different strategies to diminish healthcare associated costs. As an extension to this stream of research, the present three paper dissertation addresses the issue of reducing elevated healthcare costs using analytics. The first paper looks at extending the benefits of auditing algorithms from mere detection of fraudulent providers to maximizing the deterrence from inappropriate behavior. Using the structure of the physicians' network, a new auditing algorithm is developed. Evaluation of the algorithm is performed using an agent-based simulation and an analytical model. A case study is also included to illustrate the application of the algorithm in the warranty domain. The second paper relies on experimental data to build a personalized medical recommender system geared towards re-enforcing price-sensitive prescription behavior. The study analyzes the impact of time pressure, and procedure cost and prescription prevalence/popularity on the physicians' use of the system's recommendations. The third paper investigates the relationship between patients' compliance and healthcare costs. The study includes a survey of the literature along with a longitudinal analysis of patients' data to determine factors leading to patients' non-compliance, and ways to alleviate it.

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Rault, Antoine. "User privacy in collaborative filtering systems." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S019/document.

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Les systèmes de recommandation essayent de déduire les intérêts de leurs utilisateurs afin de leurs suggérer des items pertinents. Ces systèmes offrent ainsi aux utilisateurs un service utile car ils filtrent automatiquement les informations non-pertinentes, ce qui évite le problème de surcharge d’information qui est courant de nos jours. C’est pourquoi les systèmes de recommandation sont aujourd’hui populaires, si ce n’est omniprésents dans certains domaines tels que le World Wide Web. Cependant, les intérêts d’un individu sont des données personnelles et privées, comme par exemple son orientation politique ou religieuse. Les systèmes de recommandation recueillent donc des données privées et leur utilisation répandue nécessite des mécanismes de protection de la vie privée. Dans cette thèse, nous étudions la protection de la confidentialité des intérêts des utilisateurs des systèmes de recommandation appelés systèmes de filtrage collaboratif (FC). Notre première contribution est Hide & Share, un nouveau mécanisme de similarité, respectueux de la vie privée, pour la calcul décentralisé de graphes de K-Plus-Proches-Voisins (KPPV). C’est un mécanisme léger, conçu pour les systèmes de FC fondés sur les utilisateurs et décentralisés (ou pair-à-pair), qui se basent sur les graphes de KPPV pour fournir des recommandations. Notre seconde contribution s’applique aussi aux systèmes de FC fondés sur les utilisateurs, mais est indépendante de leur architecture. Cette contribution est double : nous évaluons d’abord l’impact d’une attaque active dite « Sybil » sur la confidentialité du profil d’intérêts d’un utilisateur cible, puis nous proposons une contre-mesure. Celle-ci est 2-step, une nouvelle mesure de similarité qui combine une bonne précision, permettant ensuite de faire de bonnes recommandations, avec une bonne résistance à l’attaque Sybil en question
Recommendation systems try to infer their users’ interests in order to suggest items relevant to them. These systems thus offer a valuable service to users in that they automatically filter non-relevant information, which avoids the nowadays common issue of information overload. This is why recommendation systems are now popular, if not pervasive in some domains such as the World Wide Web. However, an individual’s interests are personal and private data, such as one’s political or religious orientation. Therefore, recommendation systems gather private data and their widespread use calls for privacy-preserving mechanisms. In this thesis, we study the privacy of users’ interests in the family of recommendation systems called Collaborative Filtering (CF) ones. Our first contribution is Hide & Share, a novel privacy-preserving similarity mechanism for the decentralized computation of K-Nearest-Neighbor (KNN) graphs. It is a lightweight mechanism designed for decentralized (a.k.a. peer-to-peer) user-based CF systems, which rely on KNN graphs to provide recommendations. Our second contribution also applies to user-based CF systems, though it is independent of their architecture. This contribution is two-fold: first we evaluate the impact of an active Sybil attack on the privacy of a target user’s profile of interests, and second we propose a counter-measure. This counter-measure is 2-step, a novel similarity metric combining a good precision, in turn allowing for good recommendations,with high resilience to said Sybil attack

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Aleixo, Everton Lima. "Item-based-adp: análise e melhoramento do algoritmo de filtragem colaborativa item-based." Universidade Federal de Goiás, 2014. http://repositorio.bc.ufg.br/tede/handle/tede/4133.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Memory-based algorithms are the most popular among the collaborative filtering algorithms. They use as input a table containing ratings given by users to items, known as the rating matrix. They predict the rating given by user a to an item i by computing similarities of the ratings among users or similarities of the ratings among items. In the first case Memory-Based algorithms are classified as User-based algorithms and in the second one they are labeled as Item-based algorithms. The prediction is computed using the ratings of k most similar users (or items), also know as neighbors. Memory-based algorithms are simple to understand and to program, usually provide accurate recommendation and are less sensible to data change. However, to obtain the most similar neighbors for a prediction they have to process all the data which is a serious scalability problem. Also they are sensitive to the sparsity of the input. In this work we propose an efficient and effective Item-Based that aims at diminishing the sensibility of the Memory-Based approach to both problems stated above. The algorithm is faster (almost 50%) than the traditional Item-Based algorithm while maintaining the same level of accuracy. However, in environments that have much data to predict and few to train the algorithm, the accuracy of the proposed algorithm surpass significantly that of the traditional Item-based algorithms. Our approach can also be easily adapted to be used as User-based algorithms.
Algoritmos baseados em memória são os mais populares entre os algoritmos de filtragem colaborativa. Eles usam como entrada uma tabela contendo as avaliações feitas pelos usuários aos itens, conhecida como matriz de avaliações. Eles predizem a avaliação dada por um usuário a a um item i, computando a similaridade de avaliações entre a e outros usuários ou entre i e outros itens. No primeiro caso, os algoritmos baseados em memória são classificados como algoritmos baseados em usuários (User-based) e no segundo caso são rotulados como algoritmos baseados em itens (Item-Based). A predição é computada usando as avaliações dos k usuários (ou itens) mais similares, também conhecidos como vizinhos. Algoritmos baseados em memória são simples de entender e implementar. Normalmente produzem boas recomendações e são menos sensíveis a mudança nos dados. Entretanto, para obter os vizinhos mais similares para a predição, eles necessitam processar todos os dados da matriz, o que é um sério problema de escalabilidade. Eles também são sensíveis a densidade dos dados. Neste trabalho, nós propomos um algoritmo eficiente e eficaz baseado em itens que visa diminuir a sensibilidade dos algoritmos baseados em memória para ambos os problemas acima referidos. Esse algoritmo é mais rápido (quase 50%) do que o algoritmo baseado em itens tradicional, mantendo o mesmo nível de acurácia. Entretanto, em ambientes onde existem muitos dados para predizer e poucos para treinar o algoritmo, a acurácia do algoritmo proposto supera significativamente a do algoritmo tradicional baseado em itens. Nossa abordagem pode ainda ser facilmente adaptada para ser utilizada como o algoritmo baseado em usuários.

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Sunmark, Henrik. "Rekommendationssystem för livestreamingtjänster." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-204954.

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Användningen och behovet av rekommendationssystem i digitala tjänster har växt i takt med att utbudet i dessa blivit allt större och svårare för användare att navigera i. Rekommendationssystem används idag i allt ifrån E-handel till musikoch filmstreaming. För att förse användare med rekommendationer på objekt används en mängd olika väl beprövade algoritmer, filtreringsmetoder och datainsamlingsmetoder. Att applicera dessa i livestreamingtjänster ställer nya krav på systemen eftersom innehållet byts ut mer frekvent, helt nytt innehåll tillkommer regelbundet och explicit data samt metadata är sällan tillräcklig för att ta fram träffsäkra rekommendationer. I en fallstudie med företaget Liveguide undersöks hur rekommendationssystem kan appliceras i livestreamingtjänster med avseende på de utmaningar och krav som finns. Metoder presenteras där aktuella lösningar testas, utvärderas och anpassas till att fungera bra i livestreamingsammanhang. Slutligen föreslås tre modeller för rekommendationssystem som tagits fram utifrån det resultat metoderna leder till. För att tillfredsställa de identifierade utmaningarna inom området visade sig hybrida, mångsidiga modeller fördelaktiga i livestreaming.
The usage and demand of recommender systems in digital services has increased in line with their huge range of products, making it more difficult for users to navigate through the content. Recommender systems are used in a wide scope of digital services ranging from E-commerce to music and film streaming. In order to provide users with recommendations on objects, a variety of algorithms, filtering methods and methods of data collections are being used. Applying these in live streaming services puts new demands on such systems since the content is replaced frequently and new objects added regularly. Furthermore, livestreaming services often lack explicit data and metadata, making recommendations less accurate. In a case study with Liveguide, recommender systems are evaluated, focusing on whether they are applicable to live streaming services, respecting requirements and demands on such systems. Methods are presented which tests, evaluates and adapts existing solutions to fit in well in context of live streaming. Finally, three models for recommender systems are suggested, based on the methods result. In order to satisfy the identified challenges, hybrid models turned out to be preferable in the context.

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Lisena, Pasquale. "Knowledge-based music recommendation : models, algorithms and exploratory search." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS614.

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Représenter l'information décrivant la musique est une activité complexe, qui implique différentes sous-tâches. Ce manuscrit de thèse porte principalement sur la musique classique et étudie comment représenter et exploiter ses informations. L'objectif principal est l'étude de stratégies de représentation et de découverte des connaissances appliquées à la musique classique, dans des domaines tels que la production de base de connaissances, la prédiction de métadonnées et les systèmes de recommandation. Nous proposons une architecture pour la gestion des métadonnées de musique à l'aide des technologies du Web Sémantique. Nous introduisons une ontologie spécialisée et un ensemble de vocabulaires contrôlés pour les différents concepts spécifiques à la musique. Ensuite, nous présentons une approche de conversion des données, afin d’aller au-delà de la pratique bibliothécaire actuellement utilisée, en s’appuyant sur des règles de mapping et sur l’interconnexion avec des vocabulaires contrôlés. Enfin, nous montrons comment ces données peuvent être exploitées. En particulier, nous étudions des approches basées sur des plongements calculés sur des métadonnées structurées, des titres et de la musique symbolique pour classer et recommander de la musique. Plusieurs applications de démonstration ont été réalisées pour tester les approches et les ressources précédentes
Representing the information about music is a complex activity that involves different sub-tasks. This thesis manuscript mostly focuses on classical music, researching how to represent and exploit its information. The main goal is the investigation of strategies of knowledge representation and discovery applied to classical music, involving subjects such as Knowledge-Base population, metadata prediction, and recommender systems. We propose a complete workflow for the management of music metadata using Semantic Web technologies. We introduce a specialised ontology and a set of controlled vocabularies for the different concepts specific to music. Then, we present an approach for converting data, in order to go beyond the librarian practice currently in use, relying on mapping rules and interlinking with controlled vocabularies. Finally, we show how these data can be exploited. In particular, we study approaches based on embeddings computed on structured metadata, titles, and symbolic music for ranking and recommending music. Several demo applications have been realised for testing the previous approaches and resources

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41

Veroneze, Rosana 1982. "Tratamento de dados faltantes empregando biclusterização com imputação múltipla." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259088.

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Orientadores: Fernando José Von Zuben, Fabrício Olivetti de França.
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
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Resumo: As respostas fornecidas por sistemas de recomendação podem ser interpretadas como dados faltantes a serem imputados a partir do conhecimento dos dados presentes e de sua relação com os dados faltantes. Existem variadas técnicas de imputação de dados faltantes, sendo que o emprego de imputação múltipla será considerado neste trabalho. Também existem propostas alternativas para se chegar à imputação múltipla, sendo que se propõe aqui a biclusterização como uma estratégia eficaz, flexível e com desempenho promissor. Para tanto, primeiramente é realizada a análise de sensibilidade paramétrica do algoritmo SwarmBcluster, recentemente proposto para a tarefa de biclusterização e já adaptado, na literatura, para a realização de imputação única. Essa análise mostrou que a escolha correta dos parâmetros pode melhorar o desempenho do algoritmo. Em seguida, o SwarmBcluster é estendido para a implementação de imputação múltipla, sendo comparado com o bem-conhecido algoritmo NORM. A qualidade dos resultados obtidos é mensurada através de métricas diversas, as quais mostram que a biclusterização conduz a imputações múltiplas de melhor qualidade na maioria dos experimentos
Abstract: The answers provided by recommender systems can be interpreted as missing data to be imputed considering the knowledge associated with the available data and the relation between the available and the missing data. There is a wide range of techniques for data imputation, and this work is concerned with multiple imputation. Alternative approaches for multiple imputation have already been proposed, and this work takes biclustering as an effective, flexible and promising strategy. To this end, firstly it is performed a parameter sensitivity analysis of the SwarmBcluster algorithm, recently proposed to implement biclustering and already adapted, in the literature, to accomplish single imputation of missing data. This analysis has indicated that a proper choice of parameters may significantly improve the performance of the algorithm. Secondly, SwarmBcluster was extended to implement multiple imputation, being compared with the well-known NORM algorithm. The quality of the obtained results is computed considering diverse metrics, which reveal that biclustering guides to imputations of better quality in the majority of the experiments
Mestrado
Engenharia de Computação
Mestre em Engenharia Elétrica

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Cunha, Danilo Souza da. "Evolução de regras de associação para recomendação de produtos em comércio eletrônico." Universidade Presbiteriana Mackenzie, 2013. http://tede.mackenzie.br/jspui/handle/tede/1447.

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Fundo Mackenzie de Pesquisa
E-commerce has been growing rapidly over the past years. Various products, services, and information are constantly offered to millions of internet users. Defining an adequate strategy to offer a product to a customer is the main goal of a recommender system. To do so, the items to be offered have to take into account the interests of each customer. This association of items is a data mining task, more specifically a task called association rule mining. This dissertation investigated the use of bioinspired algorithms, particularly evolutionary and im-mune algorithms, to build associations among items of a database. Three sets of experiments were performed: an investigation into the influence of different selection and crossover mech-anisms in an evolutionary algorithm for association rule mining; the use of a probabilistic selection in the immune algorithm; and a comparison of the bioinspired algorithms with the standard deterministic algorithm called Apriori. The data bases for comparison were taken from real e-commerce applications. The results allowed the identification of a suitable combi-nation of the selection and crossover mechanisms for the evolutionary algorithm, and to iden-tify the strengths and weaknesses of all approaches when applied to real-world recommender systems.
O comércio eletrônico vem crescendo rapidamente ao longo dos últimos anos. Produtos, serviços e informações dos mais variados tipos são oferecidos todos os dias para milhares de usuários na Internet. Definir uma estratégia adequada para oferecer um produto a clientes é o objetivo dos sistemas de recomendação. Para isso leva em conta itens que podem ser ofertados considerando o interesse de cada cliente. Essa associação entre itens é uma tarefa que recai sobre a competência da mineração de dados, mais especificamente a área chamada de mineração de regras de associação. Esta dissertação investigou o uso de algoritmos bioinspirados, mais especificamente algoritmos evolutivos e imunológicos, a fim de construir associações entre os itens de uma base de dados. Foram feitos três estudos: a influência de diferentes mecanismos de seleseleção e cruzamento no algoritmo evolutivo; o uso de seleção probabilística no algoritmo imunológico; e a comparação dos algoritmos bioinspirados com o algoritmo determinístico clássico aplicado a essa tarefa, chamado de Apriori. As bases de dados para efeitos comparativos foram coletadas em lojas nacionais de comércio eletrônico. Os resulta-dos apresentados permitiram identificar uma combinação adequada dos mecanismos de sele-ção e cruzamento do algoritmo evolutivo, assim como identificar os pontos fortes e fracos dos algoritmos bioinspirados quando comparados ao algoritmo tradicional.

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43

Pascoal, Luiz Mário Lustosa. "Um método social-evolucionário para geração de rankings que apoiem a recomendação de eventos." Universidade Federal de Goiás, 2014. http://repositorio.bc.ufg.br/tede/handle/tede/4345.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
With the development of web 2.0, social networks have achieved great space on the internet, with that many users provide information and interests about themselves. There are expert systems that make use of the user’s interests to recommend different products, these systems are known as Recommender Systems. One of the main techniques of a Recommender Systems is the Collaborative Filtering (User-based) which recommends products to users based on what other similar people liked in the past. Therefore, this work presents model approximation of functions that generates rankings, that through a Genetic Algorithm, is able to learn an approximation function composed by different social variables, customized for each Facebook user. The learned function must be able to reproduce a ranking of people (friends) originally created with user’s information, that apply some influence in the user’s decision. As a case study, this work discusses the context of events through information regarding the frequency of participation of some users at several distinct events. Two different approaches on learning and applying the approximation function have been developed. The first approach provides a general model that learns a function in advance and then applies it in a set of test data and the second approach presents an specialist model that learns a specific function for each test scenario. Two proposals for evaluating the ordering created by the learned function, called objective functions A and B, where the results for both objective functions show that it is possible to obtain good solutions with the generalist and the specialist approaches of the proposed method.
Com o desenvolvimento da Web 2.0, as redes sociais têm conquistado grande espaço na internet, com isso muitos usuários acabam fornecendo diversas informações e interesses sobre si mesmos. Existem sistemas especialistas que fazem uso dos interesses do usuário para recomendar diferentes produtos, esses sistemas são conhecidos como Sistemas de Recomendação. Uma das principais técnicas de um Sistema de Recomendação é a Filtragem Colaborativa (User-based) que recomenda produtos para seus usuários baseados no que outras pessoas similares à ele tenham gostado no passado. Portanto, este trabalho apresenta um modelo de aproximação de funções geradora de rankings que, através de um Algoritmo Genético, é capaz de aprender uma função de aproximação composta por diferentes atributos sociais, personalizada para cada usuário do Facebook. A função aprendida deve ser capaz de reproduzir um ranking de pessoas (amigos) criado originalmente com informações do usuário, que exercem certa influência na decisão do usuário. Como estudo de caso, esse trabalho aborda o contexto de eventos através de informações com relação a frequência de participação de alguns usuários em vários eventos distintos. Foram desenvolvidas duas abordagens distintas para aprendizagem e aplicação da função de aproximação. A primeira abordagem apresenta um modelo generalista, que previamente aprende uma função e em seguida a aplica em um conjunto de dados de testes e a segunda abordagem apresenta um modelo especialista, que aprende uma função específica para cada cenário de teste. Também foram apresentadas duas propostas para avaliação da ordenação criada pela função aprendida, denominadas funções objetivo A e B, onde os resultados para ambas as funções objetivo A e B mostram que é possível obter boas soluções com as abordagens generalista e especialista do método proposto.

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44

Benouaret, Idir. "Un système de recommandation contextuel et composite pour la visite personnalisée de sites culturels." Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2332/document.

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Notre travail concerne les systèmes d’aide à la visite de musée et l’accès au patrimoine culturel. L’objectif est de concevoir des systèmes de recommandation, implémentés sur dispositifs mobiles, pour améliorer l’expérience du visiteur, en lui recommandant les items les plus pertinents et en l’aidant à personnaliser son parcours. Nous considérons essentiellement deux terrains d’application : la visite de musées et le tourisme. Nous proposons une approche de recommandation hybride et sensible au contexte qui utilise trois méthodes différentes : démographique, sémantique et collaborative. Chaque méthode est adaptée à une étape spécifique de la visite de musée. L’approche démographique est tout d’abord utilisée afin de résoudre le problème du démarrage à froid. L’approche sémantique est ensuite activée pour recommander à l’utilisateur des œuvres sémantiquement proches de celles qu’il a appréciées. Enfin l’approche collaborative est utilisée pour recommander à l’utilisateur des œuvres que les utilisateurs qui lui sont similaires ont aimées. La prise en compte du contexte de l’utilisateur se fait à l’aide d’un post-filtrage contextuel, qui permet la génération d’un parcours personnalisé dépendant des œuvres qui ont été recommandées et qui prend en compte des informations contextuelles de l’utilisateur à savoir : l’environnement physique, la localisation ainsi que le temps de visite. Dans le domaine du tourisme, les points d’intérêt à recommander peuvent être de différents types (monument, parc, musée, etc.). La nature hétérogène de ces points d’intérêt nous a poussé à proposer un système de recommandation composite. Chaque recommandation est une liste de points d’intérêt, organisés sous forme de packages, pouvant constituer un parcours de l’utilisateur. L’objectif est alors de recommander les Top-k packages parmi ceux qui satisfont les contraintes de l’utilisateur (temps et coût de visite par exemple). Nous définissons une fonction de score qui évalue la qualité d’un package suivant trois critères : l’appréciation estimée de l’utilisateur, la popularité des points d’intérêt ainsi que la diversité du package et nous proposons un algorithme inspiré de la recherche composite pour construire la liste des packages recommandés. L’évaluation expérimentale du système que nous avons proposé, en utilisant un data-set réel extrait de Tripadvisor démontre sa qualité et sa capacité à améliorer à la fois la précision et la diversité des recommandations
Our work concerns systems that help users during museum visits and access to cultural heritage. Our goal is to design recommender systems, implemented in mobile devices to improve the experience of the visitor, by recommending him the most relevant items and helping him to personalize the tour he makes. We consider two mainly domains of application : museum visits and tourism. We propose a context-aware hybrid recommender system which uses three different methods : demographic, semantic and collaborative. Every method is adapted to a specific step of the museum tour. First, the demographic approach is used to solve the problem of the cold start. The semantic approach is then activated to recommend to the user artworks that are semantically related to those that the user appreciated. Finally, the collaborative approach is used to recommend to the user artworks that users with similar preferences have appreciated. We used a contextual post filtering to generate personalized museum routes depending on artworks which were recommended and contextual information of the user namely : the physical environment, the location as well as the duration of the visit. In the tourism field, the items to be recommended can be of various types (monuments, parks, museums, etc.). Because of the heterogeneous nature of these points of interest, we proposed a composite recommender system. Every recommendation is a list of points of interest that are organized in a package, where each package may constitute a tour for the user. The objective is to recommend the Top-k packages among those who satisfy the constraints of the user (time, cost, etc.). We define a scoring function which estimates the quality of a package according to three criteria : the estimated appreciation of the user, the popularity of points of interest as well as the diversity of packages. We propose an algorithm inspired by composite retrieval to build the list of recommended packages. The experimental evaluation of the system we proposed using a real world data set crawled from Tripadvisor demonstrates its quality and its ability to improve both the relevance and the diversity of recommendations

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45

Kaufman, Jaime C. "A Hybrid Approach to Music Recommendation: Exploiting Collaborative Music Tags and Acoustic Features." UNF Digital Commons, 2014. http://digitalcommons.unf.edu/etd/540.

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Recommendation systems make it easier for an individual to navigate through large datasets by recommending information relevant to the user. Companies such as Facebook, LinkedIn, Twitter, Netflix, Amazon, Pandora, and others utilize these types of systems in order to increase revenue by providing personalized recommendations. Recommendation systems generally use one of the two techniques: collaborative filtering (i.e., collective intelligence) and content-based filtering. Systems using collaborative filtering recommend items based on a community of users, their preferences, and their browsing or shopping behavior. Examples include Netflix, Amazon shopping, and Last.fm. This approach has been proven effective due to increased popularity, and its accuracy improves as its pool of users expands. However, the weakness with this approach is the Cold Start problem. It is difficult to recommend items that are either brand new or have no user activity. Systems that use content-based filtering recommend items based on extracted information from the actual content. A popular example of this approach is Pandora Internet Radio. This approach overcomes the Cold Start problem. However, the main issue with this approach is its heavy demand on computational power. Also, the semantic meaning of an item may not be taken into account when producing recommendations. In this thesis, a hybrid approach is proposed by utilizing the strengths of both collaborative and content-based filtering techniques. As proof-of-concept, a hybrid music recommendation system was developed and evaluated by users. The results show that this system effectively tackles the Cold Start problem and provides more variation on what is recommended.

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46

Chen, Yu-Hung, and 陳昱紘. "Mining Classification Rules by ACO Algorithm in Recommender System." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/05153791957888523735.

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碩士
國立成功大學
工程科學系專班
96
Because of daily advancement of information technology and the exponential growth of data, traditional recommender system architecture can't do efficient and effective recommendation due to data matrix's sparsity and extension. And we propose a system architecture which combines Ant classification algorithm with recommender system and utilize Ant classification algorithm as data pre-processing module to find the relationship between users and movies in the recommendation database. For example, people belong to which gender, occupation, and age will like which genre of movie. System will collect these kinds of data from recommendation database and use these data as the input of recommendation for similarity and prediction computation. The goal of this system is to filter more valuable data for recommender and improve the accuracy and speed of online recommendation. We will use a movie recommendation database as experiment data which contains about one million of rating records and can be seen as an m by n user-item rating matrix. We will divide the recommendation database into four kinds of data each have various limitation of rating and time value. Then we input these data into Ant classification tool for mining some useful rules and use the new rules to collect classified data from original recommendation database for correlation or similarity computation. Finally we will evaluate the correctness and speed of the new mining data and original one with five different Collaborative Filtering algorithms.

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47

Kuo, Nai-Hao, and 郭乃豪. "Using Virus Optimization Algorithm on Collaborative Filtering Recommender System." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/r4mqbx.

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碩士
元智大學
工業工程與管理學系
106
Collaborative filtering, also known as CF, used the active data to analyze the preference of the user, and tried to make a recommendation. There are three kinds of mechanisms to create CF recommender systems: user-based, item-based, and model-based. The user-based and item-based CF systems use the rating data or basic data of users to analyze the correlation among users and provide the recommendation in real time. Yet, the model-based CF uses the historical data to train the model and offers the recommendation after the modeling is finished. In the past year, researchers are trying to create new methods to establish the recommender system or to improve the performance of the existing system. Some researches employed metaheuristic algorithms such as genetic algorithm or artificial immune system for the recommender system. Take the advantage of the complex mechanism, these metaheuristics are usually able to provide better prediction precision and higher efficiency. In this thesis, a recently developed metaheuristic algorithm, called virus optimization algorithm, is proposed to combine with collaborative filtering concept for the application of the recommender system. The popular movie recommendation database, MovieLens, is used to validate the performance of the proposed algorithm. A detailed design of experiments are implemented to find the best parameters of the algorithms and database. The best performance is then compared with several methods in the literature. The results show that the proposed VOA_RS outperforms most of algorithms in the literature and performs competitively to the rest. This study has successfully shown the merit of the VOA on the recommender system and its related applications.

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48

Cheng, Hung-Lien, and 程閎廉. "A Hybrid Collaborative Filtering Recommender System Based on Clustering Algorithm." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/52770657142827560926.

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碩士
國立中興大學
資訊科學與工程學系所
98
Collaborative recommender is one of the most popular recommendation techniques. Traditional collaborative filtering approach mainly employs a matrix of user’s ratings on items to calculate the similarity between users. If the features of users or items are provided in the data set in addition to the rating data, then those features can be used to improve the quality of recommendations. In this thesis, we proposed a hybrid recommender system based on clustering and collaborative filtering techniques. In the proposed system, items are clustered based on item features and user-item rating matrix. Similarly, users are clustered based on the user’s preferred categories of items and user-item rating matrix. Then a hybrid method that combines content-based and collaborative filtering is proposed to predict the rating of an item for a given user. The experimental results show that the proposed method has higher accuracy in terms of mean absolute error than that of User-based collaborative filtering approach, Item-based filtering approach, Clustering Items for Collaborative Filtering (CICF), and the User Profile Clustering (UPC) method. Especially, when the dataset is sparse, the accuracy of the proposed method is better and more stable than the other methods.

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Cunha, Tiago Daniel Sá. "Recommending Recommender Systems: tackling the Collaborative Filtering algorithm selection problem." Doctoral thesis, 2019. https://hdl.handle.net/10216/125150.

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Cunha, Tiago Daniel Sá. "Recommending Recommender Systems: tackling the Collaborative Filtering algorithm selection problem." Tese, 2019. https://hdl.handle.net/10216/125150.

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