Don't look stupid: avoiding pitfalls when recommending research papers
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Introduction to Information Retrieval
Introduction to Information Retrieval
Integrating tags in a semantic content-based recommender
Proceedings of the 2008 ACM conference on Recommender systems
Recommending scientific articles using citeulike
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative filtering recommender systems
The adaptive web
Content-based recommendation systems
The adaptive web
Capturing implicit user influence in online social sharing
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Collaborative filtering in social tagging systems based on joint item-tag recommendations
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Latent subject-centered modeling of collaborative tagging: An application in social search
ACM Transactions on Management Information Systems (TMIS)
Modelling user behaviour and interactions: augmented cognition on the social web
FAC'11 Proceedings of the 6th international conference on Foundations of augmented cognition: directing the future of adaptive systems
Discovery of Web user communities and their role in personalization
User Modeling and User-Adapted Interaction
A modified random walk framework for handling negative ratings and generating explanations
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Combining social information for academic networking
Proceedings of the 2013 conference on Computer supported cooperative work
Visualizing recommendations to support exploration, transparency and controllability
Proceedings of the 2013 international conference on Intelligent user interfaces
Tag Based Recommender System for Social Bookmarking Sites
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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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.