JZUS - Journal of Zhejiang University SCIENCE

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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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.

Reference

[1] Baluja, S., Seth, R., Sivakumar, D., Jing, Y., Yagnik, J., Kumar, S., Ravichandran, D., Aly, M., 2008. Video Suggestion and Discovery for YouTube: Taking Random Walks through the View Graph. Proc. 17th Int. Conf. on WWW, Beijing, China, p.895-904.

[2] Brand, M., 2005. A Random Walks Perspective on Maximizing Satisfaction and Profit. Proc. SDM, p.12-19.

[3] Breese, J.S., Heckerman, D., Kadie, C.M., 1998. Empirical Analysis of Predictive Algorithms for Collaborative Filtering. Proc. 14th Conf. on Uncertainty in Artificial Intelligence, University of Wisconsin Business School, Madison, Wisconsin, USA, p.43-52.

[4] Cheng, H., Tan, P.N., Sticklen, J., Punch, W.F., 2007. Recommendation via Query Centered Random Walk on K-partite Graph. Proc. 7th IEEE Int. Conf. on Data Mining, Omaha, Nebraska, USA, p.457-462.

[5] Doyle, P., Snell, L., 1984. Random Walks and Electrical Networks. Mathematical Association of America.

[6] Fouss, F., Pirotte, A., Renders, J.M., Saerens, M., 2007. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans. Knowl. Data Eng., 19(3):355-369.

[7] Fuxman, A., Tsaparas, P., Achan, K., Agrawal, R., 2008. Using the Wisdom of the Crowds for Keyword Generation. Proc. 17th Int. Conf on WWW, Beijing, China, p.61-70.

[8] George, T., Merugu, S., 2005. A Scalable Collaborative Filtering Framework Based on Co-clustering. Proc. 5th IEEE Int. Conf. on Data Mining, Houston, Texas, USA, p.625-628.

[9] Gori, M., Pucci, A., 2007. ItemRank: A Random-walk Based Scoring Algorithm for Recommender Engines. Proc. 20th Int. Joint Conf. on Artificial Intelligence, Hyderabad, India, p.2766-2771.

[10] Huang, Z., Chen, H., Zeng, D.D., 2004. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst., 22(1):116-142.

[11] Kunegis, J., Schmidt, S., 2007. Collaborative Filtering using Electrical Resistance Network Models. 7th Industrial Conf. on Data Mining, Leipzig, Germany, p.269-282.

[12] Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J., 2001. Item-based Collaborative Filtering Recommendation Algorithms. Proc. 10th Int. Conf. on WWW, Hong Kong, China, p.285-295.

[13] Singh, A.P., Gunawardana, A., Meek, C., Surendran, A.C., 2007. Recommendations Using Absorbing Random Walks. North East Student Colloquium on Artificial Intelligence (NESCAI).

[14] Symeonidis, P., Nanopoulos, A., Papadopoulos, A.N., Manolopoulos, Y., 2008. Nearest-biclusters collaborative filtering based on constant and coherent values. Inf. Retr., 11(1):51-75.

[15] Wang, J., Vries, A., Reinders, M., 2006. Unifying User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion. Proc. 29th ACM SIGIR Conf., p.501-508.

[16] Yildirim, H., Krishnamoorthy, M.S., 2008. A Random Walk Method for Alleviating the Sparsity Problem in Collaborative Filtering. Proc. 2008 ACM Conf. on Recommender Systems, Lausanne, Switzerland, p.131-138.

[17] Zhu, X., Ghahramani, Z., Lafferty, J., 2003. Semi-supervised Learning Using Gaussian Fields and Harmonic Functions. Proc. 20th Int. Conf. on Machine Learning, Washington DC, USA, p.912-919.

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