Matrix factorization, application to preference prediction in recommender systems
Identifieur interne : 000293 ( Main/Exploration ); précédent : 000292; suivant : 000294Matrix factorization, application to preference prediction in recommender systems
Auteurs : Julien Delporte [France]Source :
Descripteurs français
- mix :
- Wicri :
- topic : Recommandation.
English descriptors
Abstract
This thesis focuses on large scale optimization problems and especially on matrix factorization methods for large scale problems. The purpose of such methods is to extract some latent variables which will explain the data in smaller dimension space. We use our methods to address the problem of preference prediction in the framework of the recommender systems. Our first contribution focuses on matrix factorization methods applied in context-aware recommender systems problems, and particularly in socially-aware recommandation.We also address the problem of model selection for matrix factorization which ails to automatically determine the rank of the factorization.
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The purpose of such methods is to extract some latent variables which will explain the data in smaller dimension space. We use our methods to address the problem of preference prediction in the framework of the recommender systems. 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