Joint Item-Tag Recommendation Framework for Collaborative Filtering in Social Tagging Systems

41 Pages Posted: 8 Jul 2011

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

Huimin Zhao

University of Wisconsin - Milwaukee - Sheldon B. Lubar School of Business

Fei-Yue Wang

Chinese Academy of Sciences - Institute of Automation

Date Written: 2011

Abstract

Tapping into the wisdom of the crowd, social tagging is becoming an increasingly important mechanism for organizing and discovering information on the Web. Effective tag-based recommendation of information items is one of the key technologies contributing to the success of this social information discovery mechanism. A precise understanding of the information structure of social tagging systems lies at the core of an effective tag-based item recommendation method. While most existing methods either implicitly or explicitly assume a simple tripartite graph structure, in this paper, we propose a comprehensive data model to capture all types of co-occurrence information in the social tagging context. Based on this data model, we further propose a unified user profiling scheme to make full use of all available information. Finally, supported by this user profile, we propose a framework for collaborative filtering in social tagging systems. In this framework, we first generate joint item-tag recommendations, with tags indicating topical interests of users in target items. These joint recommendations are then refined by the wisdom from the crowd and projected to the item (or tag) space for final item (or tag) recommendations. Empirical evaluation using real-world data demonstrates the utility of our proposed approach.

Keywords: Collaborative filtering, social tagging, tagging structure, joint item-tag recommendation, design science

Suggested Citation

Peng, Jing and Zeng, Daniel D. and Zhao, Huimin and Wang, Fei-Yue, Joint Item-Tag Recommendation Framework for Collaborative Filtering in Social Tagging Systems (2011). Available at SSRN: https://ssrn.com/abstract=1881702 or http://dx.doi.org/10.2139/ssrn.1881702

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

Huimin Zhao

University of Wisconsin - Milwaukee - Sheldon B. Lubar School of Business ( email )

P.O. Box 742
3202 N. Maryland Ave.
Milwaukee, WI 53201-0742
United States

Fei-Yue Wang

Chinese Academy of Sciences - Institute of Automation ( email )

Beijing, 100190
China

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