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

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

doi:10.1016/j.physa.2009.08.036

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Journal article

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

Zhang, Zi-Ke Department of Physics, University of Fribourg, Switzerland
Zhou, Tao Department of Physics, University of Fribourg, Switzerland - Department of Modern Physics, University of Science and Technology of China, Hefei, China
Zhang, Yi-Cheng Department of Physics, University of Fribourg, Switzerland

19.09.2009

Published in:

Physica A: Statistical Mechanics and its Applications. - 2010, vol. 389, no. 1, p. 179-186

English 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.

Faculty

Faculté des sciences et de médecine

Department

Département de Physique

Language