Probabilistic Reinforcement Rules for Item-Based Recommender Systems

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.

• Type: Conference
• Year: 2008
• Where: ECAI
• Authors: Sylvain Castagnos, Armelle Brun, Anne Boyer
• Keywords: Artificial Intelligence, Classic Cf Algorithm, ECAI 2008, Enables Recommender Systems, Item-based Collaborative Filtering