TY  - JOUR
AU  - Santander, Denis P.
AU  - Brusilovsky, Peter
T1  - Improving Collaborative Filtering in Social Tagging Systems for the Recommendation of Scientific Articles
JO  - Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on
PY  - 2010/
VL  - 1
IS  - 
SP  - 136
EP  - 142
UR  - http://dx.doi.org/10.1109/WI-IAT.2010.261
DO  - 10.1109/WI-IAT.2010.261
KW  - tagging
KW  - taggingsurvey
KW  - recommender
KW  - collaborative
KW  - social
KW  - toread
L1  - 
SN  - 978-0-7695-4191-4
N1  - CiteULike: Improving Collaborative Filtering in Social Tagging Systems for the Recommendation of Scientific Articles
N1  - 
AB  - Social tagging systems pose new challenges to 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 social tagging systems. In joining this stream of research, we have developed and evaluated two enhancements of user-based collaborative filtering algorithms to provide recommendations of articles on Cite ULike, a social tagging service for scientific articles. The result obtained after two phases of evaluation suggests that both enhancements are beneficial. Incorporating the number of raters into the algorithms, as we do in our NwCF approach, leads to an improvement of precision, while tag-based BM25 similarity measure, an alternative to Pearson correlation for calculating the similarity between users and their neighbors, increases the coverage of the recommendation process.
ER  -