A random walk model for item recommendation in folksonomies

Zhu Zhang, Daniel Zeng, Ahmed Abbasi, Jing Peng

Research output: Contribution to conferencePaper

Abstract

Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web2.0 applications. The tags provide a new type of information that can be exploited by recommender systems. Nevertheless, the sparsity of ternary <user, tag, item> interaction data limits the performance of tag-based collaborative filtering. This paper proposes a random-walk-based algorithm to deal with the sparsity problem in social tagging data, which captures the potential transitive associations between users and items through their interaction with tags. In particular, two smoothing strategies are presented from both the user-centric and item-centric perspectives. Experiments on real-world data sets empirically demonstrate the efficacy of the proposed algorithm.

Original languageEnglish (US)
Pages121-126
Number of pages6
StatePublished - Jan 1 2011
Event21st Workshop on Information Technologies and Systems, WITS 2011 - Shanghai, China
Duration: Dec 3 2011Dec 4 2011

Other

Other21st Workshop on Information Technologies and Systems, WITS 2011
CountryChina
CityShanghai
Period12/3/1112/4/11

Keywords

  • Random walk
  • Recommender systems
  • Social tagging
  • Sparsity

ASJC Scopus subject areas

  • Information Systems

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  • Cite this

    Zhang, Z., Zeng, D., Abbasi, A., & Peng, J. (2011). A random walk model for item recommendation in folksonomies. 121-126. Paper presented at 21st Workshop on Information Technologies and Systems, WITS 2011, Shanghai, China.