Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
A recursive prediction algorithm for collaborative filtering recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Proceedings of the 2007 international ACM conference on Supporting group work
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Improved recommendation based on collaborative tagging behaviors
Proceedings of the 13th international conference on Intelligent user interfaces
Social ranking: uncovering relevant content using tag-based recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Integrating tags in a semantic content-based recommender
Proceedings of the 2008 ACM conference on Recommender systems
Tagsplanations: explaining recommendations using tags
Proceedings of the 14th international conference on Intelligent user interfaces
Learning to recognize valuable tags
Proceedings of the 14th international conference on Intelligent user interfaces
Tagommenders: connecting users to items through tags
Proceedings of the 18th international conference on World wide web
TagRec: Leveraging Tagging Wisdom for Recommendation
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
TagiCoFi: tag informed collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Using Tag Co-occurrence for Recommendation
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions
Artificial Intelligence Review
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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User-based collaborative filtering is one of the most widely-used recommendation methods. It recommends items to a user based on her similar users' preferences. The essential part of user-based collaborative filtering is to infer users' similarities. A common method is to compute the similarity between two users according to their ratings to co-rated items. In many cases, two users rate only few common items, such that the similarity between them is inaccurate and it results in misleading recommendations. With the boost of social tagging systems, exploiting social tag information has been a popular way to improve recommender systems in recent years. In this paper, we propose a novel method to compute users' similarities using inferred tag ratings. A user's preference for a tag t can be inferred upon her ratings of items tagged with t. In a case that a user rates few such items, then the inferred rating to t may be inaccurate. Hence the relationships among tags are taken into consideration to compute her preference for t according to her all item ratings, such that the inferred preference of the user could be more accurate. Evaluations were done on the MovieLens data set. The results indicate that our method can outperform the traditional user-based collaborative filtering.