GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
Enhancing privacy and preserving accuracy of a distributed collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
A Client/Server User-Based Collaborative Filtering Algorithm: Model and Implementation
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Using quantitative association rules in collaborative filtering
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
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Since the beginning of the 1990's, the Internet has constantly grown, proposing more and more services and sources of information. The challenge is no longer to provide users with data, but to improve the human/computer interactions in information systems by suggesting fair items at the right time. 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 show that it may be difficult to guarantee a good accuracy for the high values of prediction when ratings are not enough shared out on the rating scale. Thus, we propose a new approach combining a classic CF algorithm with an item association model to get better predictions. We deal with this issue by exploiting probalistic skewnesses in triplets of items. We validate our model by using the MovieLens dataset and get a significant improvement as regards the High MAE measure.