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
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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
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- English
- Classification
- Physics
- License
- License undefined
- Identifiers
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- RERO DOC 18151
- DOI 10.1016/j.physa.2009.08.036
- Persistent URL
- https://folia.unifr.ch/unifr/documents/301525
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