Collaborative filtering with user ratings and tags

Tengfei Bao, Yong Ge, Enhong Chen, Hui Xiong, Jilei Tian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 1st International Workshop on Context Discovery and Data Mining, ContextDD'12
DOIs
StatePublished - 2012
Externally publishedYes
Event1st International Workshop on Context Discovery and Data Mining, ContextDD'12 in Conjunction with the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2012 - Beijing, China
Duration: Aug 12 2012Aug 16 2012

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference1st International Workshop on Context Discovery and Data Mining, ContextDD'12 in Conjunction with the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2012
Country/TerritoryChina
CityBeijing
Period8/12/128/16/12

ASJC Scopus subject areas

  • Software
  • Information Systems

Fingerprint

Dive into the research topics of 'Collaborative filtering with user ratings and tags'. Together they form a unique fingerprint.

Cite this