Factorisation en matrices non négatives pour le filtrage collaboratif

Keywords

Humanities, Political science, Art

References

• Application of Dimensionality Reduction in Recommender System - A Case StudyBadrul Sarwar, George Karypis, Joseph A. Konstan — 2000
• Learning the parts of objects by non-negative matrix factorizationDaniel D. Lee, H. Sebastian Seung — 1999
• Evaluating collaborative filtering recommender systemsJonathan L. Herlocker, Joseph A. Konstan, Loren Terveen — 2004
• Document clustering based on non-negative matrix factorizationWei Xu, Xin Liu, Yihong Gong — 2003
• Latent semantic models for collaborative filteringThomas Hofmann — 2004
• A joint framework for collaborative and content filteringJustin Basilico, Thomas Hofmann — 2004
• Document clustering using nonnegative matrix factorizationFarial Shahnaz, Michael W. Berry, V. Paúl Pauca — 2006
• Collaborative Filtering: A Machine Learning PerspectiveBenjamin M. Marlin — 2004
• Algorithms for Non-negative Matrix FactorizationDaniel D. Lee, H. Sebastian Seung — 2000
• Modeling User Rating Profiles For Collaborative FilteringBenjamin M. Marlin — 2003
• Weighted Low-Rank ApproximationsNathan Srebro, Tommi Jaakkola — 2003
• BibliographieDenyse Baillargeon, Marie-Josée Béchard, Y. Gendron — 1929
• ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATIOND Seung, Lin-Wen Lee — 2001
• An MDP-Based Recommender SystemGuy Shani, Ronen I. Brafman, David Heckerman — 2002

Cited by

• Filtrage Collaboratif avec un Algorithme d'OrdonnancementJean-François Pessiot, Tuong Vinh Truong, Nicolas Usunier — 2007
• Factorisation matricielle, application à la recommandation personnalisée de préférences. (Matrix factorization, application to preference prediction in recommender systems)Julien Delporte — 2014
• Learning to Rank for Collaborative FilteringJean-François Pessiot, Tuong Vinh Truong, Nicolas Usunier — 2007
• Comparative analysis of neighborhood-based approache and matrix factorization in Recommender systemsОleg Chertov, Armelle Brun, Anne E. Boyer — 2015
• Can Latent Features Be Interpreted as Users in Matrix Factorization-Based Recommender Systems?Armelle Brun, Marharyta Aleksandrova, Anne E. Boyer — 2014
• présentée et soutenue publiquement parÉcole Doctorale, Des Sciences, De La — 2010
• Vers une approche comportementale de recommandation : apport de l'analyse des usages dans un processus de personnalisationIlham Esslimani — 2010
• What about Interpreting Features in Matrix Factorization-based Recommender Systems as Users?Marharyta Aleksandrova, Armelle Brun, Anne Boyer — 2014
• Identifying representative users in matrix factorization-based recommender systems: application to solving the content-less new item cold-start problemMarharyta Aleksandrova, Armelle Brun, Anne E. Boyer — 2017
• Matrix Factorization and Contrast Analysis Techniques for Recommendation. (Factorisation de matrices et analyse de contraste pour la recommandation)Marharyta Aleksandrova — 2017
• Sur les traces du futur : entre comprendre et predireArmelle Brun — 2018
• COMPARATIVE ANALYSIS OF NEIGHBORHOOD- BASED APPROACHE ANDO. Chertov, A. Brun, A. Boyer — 2015

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