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CLC number: TP393.09

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Received: 2009-01-08

Revision Accepted: 2009-04-30

Crosschecked: 2009-05-22

Cited: 3

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.7 P.927-936

http://doi.org/10.1631/jzus.A0920021


Random walk models for top-N recommendation task


Author(s):  Yin ZHANG, Jiang-qin WU, Yue-ting ZHUANG

Affiliation(s):  School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   zhangyin98@cs.zju.edu.cn

Key Words:  Random walk, Bipartite graph, Top-N recommendation, Semi-supervised learning


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Yin ZHANG, Jiang-qin WU, Yue-ting ZHUANG. Random walk models for top-N recommendation task[J]. Journal of Zhejiang University Science A, 2009, 10(7): 927-936.

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Abstract: 
Recently there has been an increasing interest in applying random walk based methods to recommender systems. We employ a Gaussian random field to model the top-N recommendation task as a semi-supervised learning problem, taking into account the degree of each node on the user-item bipartite graph, and induce an effective absorbing random walk (ARW) algorithm for the top-N recommendation task. Our random walk approach directly generates the top-N recommendations for individuals, rather than predicting the ratings of the recommendations. Experimental results on the two real data sets show that our random walk algorithm significantly outperforms the state-of-the-art random walk based personalized ranking algorithm as well as the popular item-based collaborative filtering method.

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Reference

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