TagRec: Leveraging Tagging Wisdom for Recommendation

—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 i...

• Type: Conference
• Year: 2009
• Where: CSE
• Authors: Tom Chao Zhou, Hao Ma, Irwin King, Michael R. Lyu
• Keywords: Computer Science And Engineering, CSE 2009, Large Datasets, Recommender Systems, Users Overcome Information