IEEE Transactions on Knowledge and Data Engineering
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Integrating tags in a semantic content-based recommender
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
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
Exploiting user interests for collaborative filtering: interests expansion via personalized ranking
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Cost-aware travel tour recommendation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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User ratings and tags are becoming largely available on Internet. While people usually exploit user ratings for developing recommender systems, the use of tag information in recommender systems remains under-explored. In particular, it is not clear how to use both user ratings and user tags in a complementary way to maximize the performances of recommender systems. To this end, we propose a novel collaborative filtering model based on probabilistic matrix factorization to predict users' interests to items by simultaneously utilizing both tag and rating information. Specifically, we first perform low-rank approximation for three matrices at the same time to learn the low-dimensional latent features of users, items and tags. Then, we predict one user's preference to an item as the product of the user and item latent features. Finally, experimental results on real-world data show that the proposed model can significantly outperform benchmark methods.