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

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 persona...

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Bibliographic Details
Published in:Physica A 2010, Vol.389 (1), p.179-186
Main Authors: Zhang, Zi-Ke, Zhou, Tao, Zhang, Yi-Cheng
Format: Article
Language:English
Subjects:
Accessories
Algorithms
Benchmarking
Complex networks
Diffusion
Folksonomy
Graphs
Infophysics
Personalized
Personalized recommendation
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Summary: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.
ISSN:0378-4371
1873-2119
DOI:10.1016/j.physa.2009.08.036