Factorisation matricielle, application à la recommandation personnalisée de préférences

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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Julien Delporte. Factorisation matricielle, application à la recommandation personnalisée de préférences. Autre [cs.OH]. INSA de Rouen, 2014. Français. ⟨NNT : 2014ISAM0002⟩. ⟨tel-01005223⟩

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