Personalized recommendation via integrated diffusion on user–item–tag tripartite graphs

Zi-Ke Zhang, Tao Zhou and Yi-Cheng Zhang

Physica A: Statistical Mechanics and its Applications, 2010, vol. 389, issue 1, 179-186

Abstract: Personalized recommender systems are confronting great challenges of accuracy, diversification and novelty, especially when the data set is sparse and lacks accessorial information, such as user profiles, item attributes and explicit ratings. Collaborative tags contain rich information about personalized preferences and item contents, and are therefore potential to help in providing better recommendations. In this article, we propose a recommendation algorithm based on an integrated diffusion on user–item–tag tripartite graphs. We use three benchmark data sets, Del.icio.us, MovieLens and BibSonomy, to evaluate our algorithm. Experimental results demonstrate that the usage of tag information can significantly improve accuracy, diversification and novelty of recommendations.

Keywords: Complex networks; Personalized recommendation; Diffusion; Infophysics; Folksonomy (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:389:y:2010:i:1:p:179-186

DOI: 10.1016/j.physa.2009.08.036

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