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
Leveraging tagging for neighborhood-aware probabilistic matrix factorization
Proceedings of the 21st ACM international conference on Information and knowledge management
Improving recommendation accuracy based on item-specific tag preferences
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
TaskRec: probabilistic matrix factorization in task recommendation in crowdsourcing systems
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
A novel user-based collaborative filtering method by inferring tag ratings
ACM SIGAPP Applied Computing Review
Task recommendation in crowdsourcing systems
Proceedings of the First International Workshop on Crowdsourcing and Data Mining
Collaborative filtering based on rating psychology
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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Due to the exponential growth of information on the Web, Recommender Systems have been developed to generate suggestions to help users overcome information overload and sift through huge amounts of information efficiently. Many existing approaches to recommender systems can neither handle very large datasets nor easily deal with users who have made very few ratings. Moreover, traditional recommender systems consider only the rating information, resulting in the loss of flexibility. Tagging has recently emerged as a popular way for users to annotate, organize and share resources on the Web. Several research tasks have shown that tags can represent users’ judgments about Web contents quite accurately. In the light of the facts that both the rating activity and tagging activity can reflect users’ opinions, this paper proposes a factor analysis approach called TagRec based on a unified probabilistic matrix factorization by utilizing both users’ tagging information and rating information. The complexity analysis indicates that our approach can be applied to very large datasets. Furthermore, experimental results on MovieLens data set show that our method performs better than the state-of-the-art approaches.