Personalized Recommendation via Integrated Diffusion on User-Item-Tag Tripartite Graphs

Details Personalized Recommendation via Integrated Diffusion on User-Item-Tag Tripartite Graphs Personalized Recommendation via Integrated Diffusion on User-Item-Tag Tripartite Graphs

2009-04-13

arxiv.org/abs/0904.1989v1

Physica A 389 (2010) 179-186

Computer Science

Information Retrieval

12 pages, 6 figures, 2 tables

Scientific paper

10.1016/j.physa.2009.08.036

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 paper, 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.

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