Probabilistic Reinforcement Rules for Item-Based Recommender Systems
Sylvain Castagnos, Armelle Brun, Anne Boyer · Frontiers in artificial intelligence and applications · 2008
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.