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Probabilistic Reinforcement Rules for Item-Based Recommender Systems

Sylvain Castagnos 1, * Armelle Brun 1 Anne Boyer 1 
* Corresponding author
1 KIWI - Knowledge Information and Web Intelligence
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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.
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Submitted on : Sunday, October 12, 2008 - 11:25:14 PM
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  • HAL Id : inria-00329560, version 1



Sylvain Castagnos, Armelle Brun, Anne Boyer. Probabilistic Reinforcement Rules for Item-Based Recommender Systems. 18th European Conference on Artificial Intelligence (ECAI 2008), University of Patras, Jul 2008, Patras, Greece. ⟨inria-00329560⟩



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