Collaborative filtering for social tagging systems: an experiment with CiteULike
Collaborative tagging systems pose new challenges to the developers of recommender systems. As observed by recent research, traditional implementations of classic recommender approaches, such as collaborative filtering, are not working well in this new context. To address these challenges, a number of research groups worldwide work on adapting these approaches to the specific nature of collaborative tagging systems. In joining this stream of research, we have developed and compared three variants of user-based collaborative filtering algorithms to provide recommendations of articles on CiteULike. The first approach, Classic Collaborative filtering (CCF) uses Pearson correlation to calculate similarity between users and a classic adjusted ratings formula to rank the recommendations. The second approach, Neighbor-weighted Collaborative Filtering, takes into account the number of raters in the ranking formula of the recommendations. The third approach explores an innovative way to form the user neighborhood based on a modified version of the Okapi BM25 model over users' tags. Our results suggest that both alterations of CCF are beneficial. Incorporating the number of raters into the algorithms leads to an improvement of precision, while tag-based BM25 can be considered as an alternative to Pearson correlation to calculate the similarity between users and their neighbors.