Harvesting the Tagging Power: An Approach to Social Recommendation of Information Products

31 Pages Posted: 19 Jan 2012

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

Bing Liu

University of Illinois at Chicago

Date Written: January 18, 2012

Abstract

Social tagging, as a flexible and effective information management platform, has proliferated rapidly on the Internet and intranets in the past few years. Social recommendation, which automates the process of recommending information products of potential interest to users based on tags, can improve the user experience of information discovery and promote the consumption of information products on various kinds of content delivery sites. Although a number of tag-based social recommenders have been developed, several problems resulting from the special nature of tagging data, including tagging bias, vocabulary problem, and data sparsity, have not been adequately addressed and largely limit the performance of existing recommenders. In response to these problems, this paper proposes a tag-based semantic analysis model to capture the semantics emerging in tagging data and reformulates the recommendation problem as a constrained optimization problem on both labeled (selected) and unlabeled items. An iterative learning framework developed to solve this optimization problem combines the self-organization theory in cognitive science, the probabilistic latent semantic analysis framework in information retrieval and the positive unlabeled learning methodology in machine learning. Empirical studies using social tagging datasets from three different domains demonstrate the efficacy of the proposed method.

Keywords: social tagging, collaborative filtering, social recommendation, information discovery, knowledge management, sparsity, positive unlabeled learning

Suggested Citation

Peng, Jing and Zeng, Daniel D. and Zhao, Huimin and Liu, Bing, Harvesting the Tagging Power: An Approach to Social Recommendation of Information Products (January 18, 2012). Available at SSRN: https://ssrn.com/abstract=1987504 or http://dx.doi.org/10.2139/ssrn.1987504

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

Bing Liu

University of Illinois at Chicago ( email )

1200 W Harrison St
Chicago, IL 60607
United States

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