Tesis sobre el tema "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.
Texto completoRecommender 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.
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.
Texto completoBora, Prachi Champalal. "Runtime Algorithm Selection For Grid Environments: A Component Based Framework". Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/33823.
Texto completoMaster of Science
Bora, Prachi. "Runtime Algorithm Selection For Grid Environments: A Component Based Framework". Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/33823.
Texto completoMaster of Science
Zhang, Richong. "Probabilistic Approaches to Consumer-generated Review Recommendation". Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/19935.
Texto completoYe, Brian y 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.
Texto completoRekommendationssystem 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.
Sun, Mingxuan. "Visualizing and modeling partial incomplete ranking data". Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45793.
Texto completoGonard, François. "Cold-start recommendation : from Algorithm Portfolios to Job Applicant Matching". Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS121/document.
Texto completoThe 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
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.
Texto completoRedpath, Jennifer Louise. "Improving the performance of recommender algorithms". Thesis, Ulster University, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.535143.
Texto completoJedor, Matthieu. "Bandit algorithms for recommender system optimization". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM027.
Texto completoIn 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
Blot, Guillaume. "Élaboration, parcours et automatisation de traces et savoirs numériques". Thesis, Paris 4, 2017. http://www.theses.fr/2017PA040089.
Texto completoHow 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
Alkilicgil, Erdem. "User Modeling In Mobile Environment". Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606852/index.pdf.
Texto completos 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.
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.
Texto completoLi, Lei. "Next Generation of Recommender Systems: Algorithms and Applications". FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1446.
Texto completoAnne, 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.
Texto completoGhazanfar, Mustansar Ali. "Robust, scalable, and practical algorithms for recommender systems". Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/343761/.
Texto completoRibeiro, Marco Tulio Correia. "Multi-objective pareto-efficient algorithms for recommender systems". Universidade Federal de Minas Gerais, 2013. http://hdl.handle.net/1843/ESSA-9CHG5H.
Texto completoSistemas 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.
Ozbal, Gozde. "A Content Boosted Collaborative Filtering Approach For Movie Recommendation Based On Local &". Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610984/index.pdf.
Texto completos 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.
Salam, Patrous Ziad y 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.
Texto completoAyday, Erman. "Iterative algorithms for trust and reputation management and recommender systems". Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/45868.
Texto completoTabari, Michel y 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.
Texto completoRekommendationssystem 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.
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.
Texto completoJess, Torben. "Recommender systems and market approaches for industrial data management". Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/270103.
Texto completoMakari, Manshadi Faraz [Verfasser] y 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.
Texto completoMakari, Manshadi Faraz Verfasser] y 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.
Texto completoYao, Sirui. "Evaluating, Understanding, and Mitigating Unfairness in Recommender Systems". Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103779.
Texto completoDoctor 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.
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.
Texto completoWilliams, Alyssa. "Hybrid Recommender Systems via Spectral Learning and a Random Forest". Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/etd/3666.
Texto completoHaglund, Isac y 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.
Texto completoEtt 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.
Svebrant, Henrik y 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.
Texto completoBuzzoni, 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/.
Texto completoBouneffouf, Djallel. "DRARS, A Dynamic Risk-Aware Recommender System". Phd thesis, Institut National des Télécommunications, 2013. http://tel.archives-ouvertes.fr/tel-01026136.
Texto completoLi, Siying. "Context-aware recommender system for system of information systems". Thesis, Compiègne, 2021. http://www.theses.fr/2021COMP2602.
Texto completoWorking 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
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.
Texto completoBouayad, Lina. "Analytics and Healthcare Costs (A Three Essay Dissertation)". Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5876.
Texto completoRault, Antoine. "User privacy in collaborative filtering systems". Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S019/document.
Texto completoRecommendation 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
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.
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.
Texto completoThe 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.
Lisena, Pasquale. "Knowledge-based music recommendation : models, algorithms and exploratory search". Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS614.
Texto completoRepresenting 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
Veroneze, Rosana 1982. "Tratamento de dados faltantes empregando biclusterização com imputação múltipla". [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259088.
Texto completoDissertação (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
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.
Texto completoFundo 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.
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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Made available in DSpace on 2015-03-24T21:19:16Z (GMT). No. of bitstreams: 3 Dissertação - Luiz Mario Lustosa Pascoal - 2014.pdf: 7280181 bytes, checksum: 68a6ac0602e3e51f6e6952bbd6916150 (MD5) FunctionApproximator.zip: 2288624 bytes, checksum: 178c2e6a0b080b3d0548836974016236 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2014-08-22
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.
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.
Texto completoOur 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
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.
Texto completoChen, Yu-Hung y 陳昱紘. "Mining Classification Rules by ACO Algorithm in Recommender System". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/05153791957888523735.
Texto completo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.
Kuo, Nai-Hao y 郭乃豪. "Using Virus Optimization Algorithm on Collaborative Filtering Recommender System". Thesis, 2018. http://ndltd.ncl.edu.tw/handle/r4mqbx.
Texto completo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.
Cheng, Hung-Lien y 程閎廉. "A Hybrid Collaborative Filtering Recommender System Based on Clustering Algorithm". Thesis, 2010. http://ndltd.ncl.edu.tw/handle/52770657142827560926.
Texto completo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.
Cunha, Tiago Daniel Sá. "Recommending Recommender Systems: tackling the Collaborative Filtering algorithm selection problem". Doctoral thesis, 2019. https://hdl.handle.net/10216/125150.
Texto completoCunha, 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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