Weighted slope one predictors revisited

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.