Abstract : The Internet is constantly growing, proposing more and more services and sources of information. Modeling personal preferences enables recommender systems to identify relevant subsets of items. These systems often rely on filtering techniques based on symbolic or numerical approaches in a stochastic context. In this paper, we focus on item-based collaborative filtering (CF) techniques. We propose a new approach combining a classic CF algorithm with a reinforcement model to get a better accuracy. We deal with this issue by exploiting probabilistic skewnesses in triplets of items.
https://hal.inria.fr/inria-00329560
Contributeur : Sylvain Castagnos
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Soumis le : dimanche 12 octobre 2008 - 23:25:14
Dernière modification le : mardi 24 avril 2018 - 13:34:09
Document(s) archivé(s) le : lundi 7 juin 2010 - 19:28:20
