Exploring Information Hidden in Tags: A Subject-Based Item Recommendation Approach

6 Pages Posted: 22 Nov 2010

See all articles by Jing Peng

Jing Peng

University of Connecticut - Department of Operations & Information Management

Daniel D. Zeng

University of Arizona - Department of Management Information Systems

Date Written: December 15, 2009

Abstract

Collaborative tagging sites allow users to bookmark and annotate their favorite Web contents with tags. These tags provide a novel source of information for collaborative filtering (CF). Research on how to improve item recommendation quality leveraging tags is emerging yet information hidden in tags is far from being fully exploited. In this paper, we aim at finding informative usage patterns from tags by consistent clustering on tags using nonnegative matrix factorization. The clustered subjects, represented by weighed tag vectors, can then be used to build a subject-centered user information seeking model for item recommendation. Experiments on two real-world datasets show that our subject-based algorithms substantially outperform the traditional CF methods as well as tag-enhanced recommendation approaches reported in the literature.

Keywords: Collaborative Filtering, Collaborative Tagging, Nonnegative Matrix Factorization, Tag-Based Recommendation

Suggested Citation

Peng, Jing and Zeng, Daniel D., Exploring Information Hidden in Tags: A Subject-Based Item Recommendation Approach (December 15, 2009). Proceedings of 19th Workshop on Information Technologies and Systems, pp. 73-78, 2009, Available at SSRN: https://ssrn.com/abstract=1712792

Jing Peng (Contact Author)

University of Connecticut - Department of Operations & Information Management ( email )

368 Fairfield Road
Storrs, CT 06269-2041
United States

Daniel D. Zeng

University of Arizona - Department of Management Information Systems ( email )

AZ
United States

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