Готові списки джерел за темами / Recommender Algorithm

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Автор: Grafiati
Опубліковано: 14 березня 2023

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Статті в журналах з теми "Recommender Algorithm"

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Moses, Sharon J., and L. D. Dhinesh Babu. "Buyagain Grocery Recommender Algorithm for Online Shopping of Grocery and Gourmet Foods." International Journal of Web Services Research 15, no. 3 (July 2018): 1–17. http://dx.doi.org/10.4018/ijwsr.2018070101.

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Анотація:
Online shopping of grocery and gourmet products differ from other shopping activities due to its routine nature of buy-consume-buy. The existing recommendation algorithms of ecommerce websites are suitable only to render recommendation for products of one time purchase. So, in order to identify and recommend the products that users are likely to buy again and again, a novel recommender algorithm is proposed based on linguistic decision analysis model. The proposed buyagain recommender algorithm finds the semantic value of the user comments and computes the semantic value along with the user rating to render recommendation to the user. The efficiency of the buyagain recommender algorithm is evaluated using the grocery and gourmet dataset of amazon ecommerce websites. The end result proves that the algorithm accurately recommends the product that the user likes to purchase once again.
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2

Kavu, Tatenda D., Kudakwashe Dube, and Peter G. Raeth. "Holistic User Context-Aware Recommender Algorithm." Mathematical Problems in Engineering 2019 (September 29, 2019): 1–15. http://dx.doi.org/10.1155/2019/3965845.

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Анотація:
Existing recommender algorithms lack dynamism, human focus, and serendipitous recommendations. The literature indicates that the context of a user influences user decisions, and when incorporated in recommender systems (RSs), novel and serendipitous recommendations can be realized. This article shows that social, cultural, psychological, and economic contexts of a user influence user traits or decisions. The article demonstrates a novel approach of incorporating holistic user context-aware knowledge in an algorithm to solve the highlighted problems. Web content mining and collaborative filtering approaches were used to develop a holistic user context-aware (HUC) algorithm. The algorithm was evaluated on a social network using online experimental evaluations. The algorithm demonstrated dynamism, novelty, and serendipity with an average of 84% novelty and 85% serendipity.
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3

Mali, Mahesh, Dhirendra Mishra, and M. Vijayalaxmi. "Benchmarking for Recommender System (MFRISE)." 3C TIC: Cuadernos de desarrollo aplicados a las TIC 11, no. 2 (December 29, 2022): 146–56. http://dx.doi.org/10.17993/3ctic.2022.112.146-156.

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Анотація:
The advent of the internet age offers overwhelming choices of movies and shows to viewers which create need of comprehensive Recommendation Systems (RS). Recommendation System will suggest best content to viewers based on their choice using the methods of Information Retrieval, Data Mining and Machine Learning algorithms. The novel Multifaceted Recommendation System Engine (MFRISE) algorithm proposed in this paper will help the users to get personalized movie recommendations based on multi-clustering approach using user cluster and Movie cluster along with their interaction effect. This will add value to our existing parameters like user ratings and reviews. In real-world scenarios, recommenders have many non-functional requirements of technical nature. Evaluation of Multifaceted Recommendation System Engine must take these issues into account in order to produce good recommendations. The paper will show various technical evaluation parameters like RMSE, MAE and timings, which can be used to measure accuracy and speed of Recommender system. The benchmarking results also helpful for new recommendation algorithms. The paper has used MovieLens dataset for purpose of experimentation. The studied evaluation methods consider both quantitative and qualitative aspects of algorithm with many evaluation parameters like mean squared error (MSE), root mean squared error (RMSE), Test Time and Fit Time are calculated for each popular recommender algorithm (NMF, SVD, SVD++, SlopeOne, Co- Clustering) implementation. The study identifies the gaps and challenges faced by each above recommender algorithm. This study will also help researchers to propose new recommendation algorithms by overcoming identified research gaps and challenges of existing algorithms.
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4

Huang, Jiaquan, Zhen Jia, and Peng Zuo. "Improved collaborative filtering personalized recommendation algorithm based on k-means clustering and weighted similarity on the reduced item space." Mathematical Modelling and Control 3, no. 1 (2023): 39–49. http://dx.doi.org/10.3934/mmc.2023004.

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Анотація:
<abstract><p>Collaborative filtering (CF) algorithm is one of the most widely used recommendation algorithms in recommender systems. However, there is a data sparsity problem in the traditional CF algorithm, which may reduce the recommended efficiency of recommender systems. This paper proposes an improved collaborative filtering personalized recommendation (ICF) algorithm, which can effectively improve the data sparsity problem by reducing item space. By using the k-means clustering method to secondarily extract the similarity information, ICF algorithm can obtain the similarity information of users more accurately, thus improving the accuracy of recommender systems. The experiments using MovieLens and Netflix data set show that the ICF algorithm has a significant improvement in the accuracy and quality of recommendation.</p></abstract>
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Mısır, Mustafa, and Michèle Sebag. "Alors: An algorithm recommender system." Artificial Intelligence 244 (March 2017): 291–314. http://dx.doi.org/10.1016/j.artint.2016.12.001.

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6

Cintia Ganesha Putri, Debby, Jenq-Shiou Leu, and Pavel Seda. "Design of an Unsupervised Machine Learning-Based Movie Recommender System." Symmetry 12, no. 2 (January 21, 2020): 185. http://dx.doi.org/10.3390/sym12020185.

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Анотація:
This research aims to determine the similarities in groups of people to build a film recommender system for users. Users often have difficulty in finding suitable movies due to the increasing amount of movie information. The recommender system is very useful for helping customers choose a preferred movie with the existing features. In this study, the recommender system development is established by using several algorithms to obtain groupings, such as the K-Means algorithm, birch algorithm, mini-batch K-Means algorithm, mean-shift algorithm, affinity propagation algorithm, agglomerative clustering algorithm, and spectral clustering algorithm. We propose methods optimizing K so that each cluster may not significantly increase variance. We are limited to using groupings based on Genre and Tags for movies. This research can discover better methods for evaluating clustering algorithms. To verify the quality of the recommender system, we adopted the mean square error (MSE), such as the Dunn Matrix and Cluster Validity Indices, and social network analysis (SNA), such as Degree Centrality, Closeness Centrality, and Betweenness Centrality. We also used average similarity, computational time, association rule with Apriori algorithm, and clustering performance evaluation as evaluation measures to compare method performance of recommender systems using Silhouette Coefficient, Calinski-Harabaz Index, and Davies–Bouldin Index.
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Zhang, Heng-Ru, Fan Min, Xu He, and Yuan-Yuan Xu. "A Hybrid Recommender System Based on User-Recommender Interaction." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/145636.

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Анотація:
Recommender systems are used to make recommendations about products, information, or services for users. Most existing recommender systems implicitly assume one particular type of user behavior. However, they seldom consider user-recommender interactive scenarios in real-world environments. In this paper, we propose a hybrid recommender system based on user-recommender interaction and evaluate its performance with recall and diversity metrics. First, we define the user-recommender interaction. The recommender system accepts user request, recommendsNitems to the user, and records user choice. If some of these items favor the user, she will select one to browse and continue to use recommender system, until none of the recommended items favors her. Second, we propose a hybrid recommender system combining random andk-nearest neighbor algorithms. Third, we redefine the recall and diversity metrics based on the new scenario to evaluate the recommender system. Experiments results on the well-known MovieLens dataset show that the hybrid algorithm is more effective than nonhybrid ones.
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8

Kumar Ojha, Rajesh, and Dr Bhagirathi Nayak. "Application of Machine Learning in Collaborative Filtering Recommender Systems." International Journal of Engineering & Technology 7, no. 4.38 (December 3, 2018): 213. http://dx.doi.org/10.14419/ijet.v7i4.38.24445.

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Анотація:
Recommender systems are one of the important methodologies in machine learning technologies, which is using in current business scenario. This article proposes a book recommender system using deep learning technique and k-Nearest Neighbors (k-NN) classification. Deep learning technique is one of the most effective techniques in the field of recommender systems. Recommender systems are intelligent systems in Machine Learning that can make difference from other algorithms. This article considers application of Machine Learning Technology and we present an approach based a recommender system. We used k-Nearest Neighbors classification algorithm of deep learning technique to classify users based book recommender system. We analyze the traditional collaborative filtering with our methodology and also to compare with them. Our outcomes display the projected algorithm is more precise over the existing algorithm, it also consumes less time and reliable than the existing methods.
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Li, Wen-Jun, Yuan-Yuan Xu, Qiang Dong, Jun-Lin Zhou, and Yan Fu. "TaDb: A time-aware diffusion-based recommender algorithm." International Journal of Modern Physics C 26, no. 09 (June 22, 2015): 1550102. http://dx.doi.org/10.1142/s0129183115501028.

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Анотація:
Traditional recommender algorithms usually employ the early and recent records indiscriminately, which overlooks the change of user interests over time. In this paper, we show that the interests of a user remain stable in a short-term interval and drift during a long-term period. Based on this observation, we propose a time-aware diffusion-based (TaDb) recommender algorithm, which assigns different temporal weights to the leading links existing before the target user's collection and the following links appearing after that in the diffusion process. Experiments on four real datasets, Netflix, MovieLens, FriendFeed and Delicious show that TaDb algorithm significantly improves the prediction accuracy compared with the algorithms not considering temporal effects.
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Gelvez Garcia, Nancy Yaneth, Jesús Gil-Ruíz, and Jhon Fredy Bayona-Navarro. "Optimization of Recommender Systems Using Particle Swarms." Ingeniería 28, Suppl (February 28, 2023): e19925. http://dx.doi.org/10.14483/23448393.19925.

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Анотація:
Background: Recommender systems are one of the most widely used technologies by electronic businesses and internet applications as part of their strategies to improve customer experiences and boost sales. Recommender systems aim to suggest content based on its characteristics and on user preferences. The best recommender systems are able to deliver recommendations in the shortest possible time and with the least possible number of errors, which is challenging when working with large volumes of data. Method: This article presents a novel technique to optimize recommender systems using particle swarm algorithms. The objective of the selected genetic algorithm is to find the best hyperparameters that minimize the difference between the expected values and those obtained by the recommender system. Results: The algorithm demonstrates viability given the results obtained, highlighting its simple implementation and the minimal and easily attainable computational resources necessary for its execution. Conclusions: It was possible to develop an algorithm using the most convenient properties of particle swarms in order to optimize recommender systems, thus achieving the ideal behavior for its implementation in the proposed scenario.
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Більше джерел

Дисертації з теми "Recommender Algorithm"

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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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Анотація:
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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Анотація:
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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Анотація:
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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Анотація:
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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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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Анотація:
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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Анотація:
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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Анотація:
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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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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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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Книги з теми "Recommender Algorithm"

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Gündüz-Ögüdücü, Şule. Web page recommendation models: Theory and algorithms. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.

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Mandra, Yuliya, Elena Semencova, Sergey Griroriev, N. Gegalina, Elena Svetlakova, Maria Vlasova, Yuriy Boldyrev, Anastasiya Kotikova, Aleksandr Ivashov, and Aleksandr Legkih. MODERN METHODS OF COMPLEX TREATMENT OF PATIENTS WITH HERPES SIMPLEX LIPS. ru: TIRAZH Publishing House, 2019. http://dx.doi.org/10.18481/textbook_5dfa340500ebf6.85792235.

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The training manual is devoted to the problem of herpetic infection in dentistry and was developed taking into account world scientific and clinical practice, experience working on clinical recommendations of the Ministry of Health of the Russian Federation, as well as experimental, laboratory and clinical data obtained by the authors. This manual presents materials related to modern ideas about the etiology and pathogenesis of herpetic infection, modern diagnostic methods are highlighted, and current complex treatment algorithms are proposed, and clinical cases are presented. Recommended as a guide for practitioners of various specialties, clinical residents, senior students.
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Varlamov, Oleg. 18 examples of mivar expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1248446.

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Many years of research on mivar technologies of logical artificial intelligence have allowed us to create a new powerful, versatile and fast tool, which is called "multidimensional open gnoseological active net" — "multidimensional open gnoseological active net: MOGAN". This tool allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format, and it can be used to model cause-and-effect relationships in different subject areas and create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with "Big Knowledge". The reader, after studying this tutorial, you will be able to create mivar expert system with the help of CASMI Wi!Mi. Designed for students, bachelors, masters and postgraduate students studying artificial intelligence methods, as well as for users, experts and specialists, creating a system of information processing and management, mivar models, expert systems, automated control systems, systems of decision support and Recommender systems.
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Bleakley, Chris. Poems That Solve Puzzles. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198853732.001.0001.

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Algorithms are the hidden methods that computers apply to process information and make decisions. The book tells the story of algorithms from their ancient origins to the present day and beyond. The book introduces readers to the inventors and events behind the genesis of the world’s most important algorithms. Along the way, it explains, with the aid of examples and illustrations, how the most influential algorithms work. The first algorithms were invented in Mesopotamia 4,000 years ago. The ancient Greeks refined the concept, creating algorithms for finding prime numbers and enumerating Pi. Al-Khawrzmi’s 9th century books on algorithms ultimately became their conduit to the West. The invention of the electronic computer during World War II transformed the importance of the algorithm. The first computer algorithms were for military applications. In peacetime, researchers turned to grander challenges - forecasting the weather, route navigation, choosing marriage partners, and creating artificial intelligences. The success of the Internet in the 70s depended on algorithms for transporting data and correcting errors. A clever algorithm for ranking websites was the spark that ignited Google. Recommender algorithms boosted sales at Amazon and Netflix, while the EdgeRank algorithm drove Facebook’s NewsFeed. In the 21st century, an algorithm that mimics the operation of the human brain was revisited with the latest computer technology. Suddenly, algorithms attained human-level accuracy in object and speech recognition. An algloirthm defeated the world champion at Go - the most complex of board games. Today, algorithms for cryptocurrencies and quantum computing look set to change the world.
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Mohanty, Sachi Nandan, P. Pavan Kumar, S. Vairachilai, and Sirisha Potluri. Recommender Systems: Algorithms and Applications. Taylor & Francis Group, 2021.

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Mohanty, Sachi Nandan, P. Pavan Kumar, S. Vairachilai, and Sirisha Potluri. Recommender Systems: Algorithms and Applications. Taylor & Francis Group, 2021.

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Mohanty, Sachi Nandan, P. Pavan Kumar, S. Vairachilai, and Sirisha Potluri. Recommender Systems: Algorithms and Applications. Taylor & Francis Group, 2021.

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8

Towards Metadata-Aware Algorithms for Recommender Systems. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2010.

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Kant, Tanya. Making it Personal. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190905088.001.0001.

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The encounter of “personalized experiences”—targeted advertisements, tailored information feeds, and “recommended” content, among other things—is now a common and somewhat inescapable component of digital life. More often than not however, “you” the user are not primarily responsible for personalizing your web engagements: instead, with the help of your search, browsing, and purchase histories, your “likes,” your click-throughs, and a multitude of other data you produce as you go about your day, your experience can “conveniently”—and computationally—be personalized on your behalf. This book explores a host of new questions that emerge from web users’ encounters with these forms of algorithmic personalization. What do users “know” about the algorithms that apparently “know” them? If personalization practices seek to act on users’ behalf (for instance, by deciding what content is personally relevant), then how do users retain or relinquish their autonomy? Indeed, what kinds of selfhoods are made possible when personalization algorithms intervene in identity construction? Making It Personal is the first full-length monograph to critically analyze the sociocultural implications of algorithmic personalization through the accounts and testimonies of web users themselves. At the heart of the book are interviews and focus groups with web users who—through a myriad of resistant, tactical, resigned, or trusting engagements—encounter algorithmic personalization as part of their lived experience on the web. The book proposes that for those who encounter it, algorithmic personalization creates new implications for knowledge production, autonomy, cultural capital, and formations of self.
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Big Data Recommender Systems: Algorithms, Architectures, Big Data, Security and Trust. Institution of Engineering & Technology, 2019.

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Частини книг з теми "Recommender Algorithm"

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Kulkarni, Akshay, Adarsha Shivananda, Anoosh Kulkarni, and V. Adithya Krishnan. "Classification Algorithm–Based Recommender Systems." In Applied Recommender Systems with Python, 175–206. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8954-9_8.

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Wang, Po-Kai, Chao-Fu Hong, and Min-Huei Lin. "Interactive Genetic Algorithm Joining Recommender System." In Intelligent Information and Database Systems, 40–48. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14802-7_4.

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Miao, Huiyu, Bingqing Luo, and Zhixin Sun. "An Improved Context-Aware Recommender Algorithm." In Intelligent Computing Theories and Application, 153–62. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42291-6_15.

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Chen, Jie, Baohua Qiang, Yaoguang Wang, Peng Wang, and Jun Huang. "An Optimized Tag Recommender Algorithm in Folksonomy." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 47–56. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-662-44980-6_6.

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Lin, Pei-Chun, and Nureize Arbaiy. "An Algorithm Design of Kansei Recommender System." In Advances in Intelligent Systems and Computing, 115–23. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72550-5_12.

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Król, Dariusz, Zuzanna Zborowska, Paweł Ropa, and Łukasz Kincel. "CORDIS Partner Matching Algorithm for Recommender Systems." In Intelligent Information and Database Systems, 701–15. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21743-2_56.

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Polatidis, Nikolaos, Stelios Kapetanakis, and Elias Pimenidis. "Recommender Systems Algorithm Selection Using Machine Learning." In Proceedings of the International Neural Networks Society, 477–87. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80568-5_39.

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Singh, Suraj Pal, and Shano Solanki. "Recommender System Survey: Clustering to Nature Inspired Algorithm." In Proceedings of 2nd International Conference on Communication, Computing and Networking, 757–68. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1217-5_76.

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Chang, Na, Mhd Irvan, and Takao Terano. "An Item Influence-Centric Algorithm for Recommender Systems." In Advances in Intelligent Systems and Computing, 553–60. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07593-8_64.

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Vaishampayan, Samarth, Gururaj Singh, Vinayakprasad Hebasur, and Rupali Kute. "Market Basket Analysis Recommender System using Apriori Algorithm." In Lecture Notes in Electrical Engineering, 461–72. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9885-9_38.

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Тези доповідей конференцій з теми "Recommender Algorithm"

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De Pessemier, Toon, Kris Vanhecke, and Luc Martens. "A scalable, high-performance Algorithm for hybrid job recommendations." In the Recommender Systems Challenge. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2987538.2987539.

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Zhu, Xue, and Yuqing Sun. "Differential Privacy for Collaborative Filtering Recommender Algorithm." In CODASPY'16: Sixth ACM Conference on Data and Application Security and Privacy. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2875475.2875483.

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Yaoning Fang and Yunfei Guo. "A context-aware matrix factorization recommender algorithm." In 2013 IEEE 4th International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2013. http://dx.doi.org/10.1109/icsess.2013.6615454.

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Gupta, Utkarsh, and Nagamma Patil. "Recommender system based on Hierarchical Clustering algorithm Chameleon." In 2015 IEEE International Advance Computing Conference (IACC). IEEE, 2015. http://dx.doi.org/10.1109/iadcc.2015.7154856.

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Zhang, Liyan, Kai Zhang, and Chunping Li. "A topical PageRank based algorithm for recommender systems." In the 31st annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390334.1390465.

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Luo, Yongen, Jicheng Hu, and Xiaofeng Wei. "Blog Recommender Based on Hypergraph Modeling Clustering Algorithm." In 2013 Fourth World Congress on Software Engineering (WCSE). IEEE, 2013. http://dx.doi.org/10.1109/wcse.2013.42.

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Bansal, Saumya, and Niyati Baliyan. "Memetic Algorithm based Similarity Metric for Recommender System." In the 12th IEEE/ACM International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3368235.3369372.

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Xiong, Lei, Yang Xiang, Qi Zhang, and Lili Lin. "A Novel Nearest Neighborhood Algorithm for Recommender Systems." In 2012 Third Global Congress on Intelligent Systems (GCIS). IEEE, 2012. http://dx.doi.org/10.1109/gcis.2012.58.

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Xuan, Zhaoguo, Haoxiang Xia, and Jing Miao. "A Personalized Recommender Algorithm Based on Semantic Tree." In 2011 Fourth International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2011. http://dx.doi.org/10.1109/cso.2011.52.

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Liu, T., and D. Fan. "Random Forest Algorithm in Information Personalization Recommender System." In The International Conference on Forthcoming Networks and Sustainability (FoNeS 2022). Institution of Engineering and Technology, 2022. http://dx.doi.org/10.1049/icp.2022.2367.

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Звіти організацій з теми "Recommender Algorithm"

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Daudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe, and Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, December 2021. http://dx.doi.org/10.53328/uxuo4751.

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The report provides a review of how risk is conceived of, modelled, and mapped in studies of infectious water, sanitation, and hygiene (WASH) related diseases. It focuses on spatial epidemiology of cholera, malaria and dengue to offer recommendations for the field of WASH-related disease risk mapping. The report notes a lack of consensus on the definition of disease risk in the literature, which limits the interpretability of the resulting analyses and could affect the quality of the design and direction of public health interventions. In addition, existing risk frameworks that consider disease incidence separately from community vulnerability have conceptual overlap in their components and conflate the probability and severity of disease risk into a single component. The report identifies four methods used to develop risk maps, i) observational, ii) index-based, iii) associative modelling and iv) mechanistic modelling. Observational methods are limited by a lack of historical data sets and their assumption that historical outcomes are representative of current and future risks. The more general index-based methods offer a highly flexible approach based on observed and modelled risks and can be used for partially qualitative or difficult-to-measure indicators, such as socioeconomic vulnerability. For multidimensional risk measures, indices representing different dimensions can be aggregated to form a composite index or be considered jointly without aggregation. The latter approach can distinguish between different types of disease risk such as outbreaks of high frequency/low intensity and low frequency/high intensity. Associative models, including machine learning and artificial intelligence (AI), are commonly used to measure current risk, future risk (short-term for early warning systems) or risk in areas with low data availability, but concerns about bias, privacy, trust, and accountability in algorithms can limit their application. In addition, they typically do not account for gender and demographic variables that allow risk analyses for different vulnerable groups. As an alternative, mechanistic models can be used for similar purposes as well as to create spatial measures of disease transmission efficiency or to model risk outcomes from hypothetical scenarios. Mechanistic models, however, are limited by their inability to capture locally specific transmission dynamics. The report recommends that future WASH-related disease risk mapping research: - Conceptualise risk as a function of the probability and severity of a disease risk event. Probability and severity can be disaggregated into sub-components. For outbreak-prone diseases, probability can be represented by a likelihood component while severity can be disaggregated into transmission and sensitivity sub-components, where sensitivity represents factors affecting health and socioeconomic outcomes of infection. -Employ jointly considered unaggregated indices to map multidimensional risk. Individual indices representing multiple dimensions of risk should be developed using a range of methods to take advantage of their relative strengths. -Develop and apply collaborative approaches with public health officials, development organizations and relevant stakeholders to identify appropriate interventions and priority levels for different types of risk, while ensuring the needs and values of users are met in an ethical and socially responsible manner. -Enhance identification of vulnerable populations by further disaggregating risk estimates and accounting for demographic and behavioural variables and using novel data sources such as big data and citizen science. This review is the first to focus solely on WASH-related disease risk mapping and modelling. The recommendations can be used as a guide for developing spatial epidemiology models in tandem with public health officials and to help detect and develop tailored responses to WASH-related disease outbreaks that meet the needs of vulnerable populations. The report’s main target audience is modellers, public health authorities and partners responsible for co-designing and implementing multi-sectoral health interventions, with a particular emphasis on facilitating the integration of health and WASH services delivery contributing to Sustainable Development Goals (SDG) 3 (good health and well-being) and 6 (clean water and sanitation).
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