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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Project supported by the National Natural Science Foundation of China (Nos. 60525108 and 60533090), the National Hi-Tech Research and Development Program (863) of China (No. 2006AA010107), and the Program for Changjiang Scholars and Innovative Research Team in University, China (No. IRT0652)
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Zhang, Y., Wu, Jq. & Zhuang, Yt. Random walk models for top-N recommendation task. J. Zhejiang Univ. Sci. A 10, 927–936 (2009). https://doi.org/10.1631/jzus.A0920021
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DOI: https://doi.org/10.1631/jzus.A0920021