Bilinear models for item recommendation based on tags

He Liu, Daniel Zeng, Fen Xia, Hui Qian Li

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

Abstract

Recently, collaborative tagging has been gaining increasing popularity in a large variety of websites. The tags generated from collaborative tagging provide highly abstracted information about users' personal taste on information item, and therefore could be used to profile both users and items. However, flattening the three dimensional user-tag-item matrix into two-way matrices will lead to loss of two dimensional relationships between users, items and tags completely. In this paper, we use a predictive bilinear model to capture the informative interaction patterns among user-tag and item-tag matrices, and employ the Nonnegative Matrix Factorization (NMF) algorithm to extract lower dimensional representative features from tags. Experiments on two real-world datasets show that our approach substantially outperforms the traditional CF methods as well as tag-based recommendation methods reported in the literature.

Original languageEnglish (US)
Title of host publicationProceedings of 2010 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2010
Pages463-466
Number of pages4
DOIs
StatePublished - Oct 15 2010
Event2010 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2010 - QingDao, China
Duration: Jul 15 2010Jul 17 2010

Publication series

NameProceedings of 2010 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2010

Other

Other2010 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2010
CountryChina
CityQingDao
Period7/15/107/17/10

ASJC Scopus subject areas

  • Information Systems and Management
  • Management Science and Operations Research

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