Tags Meet Ratings: Improving Collaborative Filtering with Tag-Based Neighborhood Method

Abstract

Collaborative flltering (CF) is a method for personalized recommendation. The sparsity of rating data seriously impairs the quality of CF’s recommendation. Meanwhile, there is more and more tag information generated by online users that implies their preferences. Exploiting these tag data is a promising means to alleviate the sparsity problem. Although the intention is straight-forward, there’s no existed solution that makes full use of tags to improve the recommendation quality of traditional rating-based collaborative flltering approaches. In this paper, we propose a novel approach to fuse a tag-based neighborhood method into the traditional rating-based CF. Tag-based neighborhood method is employed to flnd similar users and items. These neighborhood information helps the sequent CF procedure produce higher quality recommendations. The experiments show that our approach outperforms the state-of-the-art ones.