GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Machine Learning
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
IIS '09 Proceedings of the 2009 International Conference on Industrial and Information Systems
An Item-based Collaborative Filtering Recommendation Algorithm Using Slope One Scheme Smoothing
ISECS '09 Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02
Userrank for item-based collaborative filtering recommendation
Information Processing Letters
Pareto-efficient hybridization for multi-objective recommender systems
Proceedings of the sixth ACM conference on Recommender systems
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Recommender systems are used to help people in specific life choices, like what items to buy, what news to read or what movies to watch. A relevant work in this context is the Slope One algorithm, which is based on the concept of differential popularity between items (i.e., how much better one item is liked than another). This paper proposes new approaches to extend Slope One based predictors for collaborative filtering, in which the predictions are weighted based on the number of users that co-rated items. We propose to improve collaborative filtering by exploiting the web of trust concept, as well as an item utility measure based on the error of predictions based on specific items to specific users. We performed experiments using three application scenarios, namely Movielens, Epinions, and Flixter. Our results demonstrate that, in most cases, exploiting the web of trust is benefitial to prediction performance, and improvements are reported when comparing the proposed approaches against the original Weighted Slope One algorithm.