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
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
Collaborative filtering recommender systems
The adaptive web
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
User-based collaborative filtering on cross domain by tag transfer learning
Proceedings of the 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining
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User-based collaborative filtering is one of the most widely-used recommender methods. It recommends items to a user according to her similar users' opinions. The key point of user-based collaborative filtering is to compute users' similarities. In traditional user-based collaborative filtering, the similarity between two users is determined by their ratings to co-rated items. In some cases, two users rate few common items, such that the similarity between them may be inaccurate and it results in misleading recommendations. With the rapid development of social tagging systems, social tagging data poses new opportunities for recommender systems. Many researchers have proposed different methods to exploit tagging data to improve the performance of recommender systems. In this paper, we propose a new approach to compute users' similarities using the inferred tag ratings. A user's preference for a tag t can be inferred based on her ratings of items tagged with t. A user rates too few such items, then her inferred rating to t may be inaccurate. Hence the relationships among tags are used to infer her preference for t based on all her item ratings, such that the preference of user could be accurate. Experiments were done on the MovieLens data set to evaluate the performance of our approach. The results show that our approach outperform traditional user-based collaborative filtering.