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New Technology of Library and Information Service  2012, Vol. 28 Issue (4): 48-53    DOI: 10.11925/infotech.1003-3513.2012.04.08
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Research on User Interest Shift Aware Random Walk with Restart Recommendation Algorithm
Yu Yan1,2, Qiu Guanghua1,3
1. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
2. Computer Science Department, Southeast University Chenxian College, Nanjing 210088, China;
3. Information Science Department, Pennsylvania State University, Malvern 19355, USA
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Abstract  Aiming at random walk with restart recommendation algorithm ignoring user interest shift, this paper propses a new random walk with restart recommendation algorithm based on user interest shift. It identifies user interest by clustering, then creates user interest model on which estimates user's current interest concerning time decay. Finally, it forms the transition probability to make recommendation according to user current interest. Experiment shows that proposed algorithm can improve the recommendation accuracy efficiently.
Key wordsInterest shift      Random walk with restart      Personalized recommendation     
Received: 23 February 2012      Published: 20 May 2012
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TP393

 

Cite this article:

Yu Yan, Qiu Guanghua. Research on User Interest Shift Aware Random Walk with Restart Recommendation Algorithm. New Technology of Library and Information Service, 2012, 28(4): 48-53.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2012.04.08     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2012/V28/I4/48

[1] Wang Z Q, Tan Y W, Zhang M. Graph-based Recommendation on Social Networks[C]. In: Proceedings of the 12th International Asia-Pacific Web Conference(APWEB), Busan. 2010:116-122.

[2] Fouss F, Pirotte A, Renders J M, et al. Radnom-walk Computation of Similarities Between Nodes of a Graph with Application to Collaborative Recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2007,19(3):355-369.

[3] Yildirim H, Krishnamoorthy M S. A Random Walk Method for Alleviating the Sparsity Problem in Collaborative Filtering[C]. In: Proceedings of the 2008 ACM Conference on Recommender Systems(RecSys'08). USA:ACM, 2008: 131-138.

[4] Konstas I, Stathopoulos V, Jose J M. On Social Networks and Collaborative Recommendation[C].In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. USA: ACM, 2009: 195-202.

[5] 俞琰,邱广华. 显式评分的重启动随机游走推荐算法研究[J]. 现代图书情报技术, 2012 (3):8-14. (Yu Yan, Qiu Guanghua. Research on Random Walk with Restart Recommendation Algorithm of Explicit Rating[J]. New Technology of Library and Information Service, 2012 (3):8-14.)

[6] Tong H H, Faloutsos C, Pan J Y. Fast Random Walk With Restart and Its Applications[C].In: Proceedings of the 6th International Conference on Data Mining(ICDM'06).USA:IEEE CPS, 2006: 613-622.

[7] Wu P, Yeung C H, Liu W, et al.Time Aware Collaborative Filtering with Piecewise Decay Functionl[J/OL]. [2012-03-01]. http://arxiv.org/abs/1010.3988.

[8] Koren Y. Collaborative Filtering with Temporal Dynamics[J]. Communications of the ACM, 2010,53(4):89-97.

[9] Cao H H, Chen E, Yang J, et al. Enhancing Recommender Systems Under Volatile Userinterest Drifts[C].In: Proceedings of the 18th ACM Conference on Information and Knowledge Mangement. USA: ACM, 2009:1257-1266.

[10] Zhang Y C, Liu Y Z. A Collaborative Filtering Algorithm Based on Time Period Partition[C]. In: Proceeding of the 3rd International Symposium on Intelligent Information Technology and Security Informatics.USA: IEEE, 2010: 777-780.

[11] Chen Z M, Jiang Y, Zhao Y. A Collaborative Filtering Recommendation Algorithm Based on User Interest Change and Trust Evaluation[J]. International Journal of Digital Content Technology and its Applications, 2010,9(4):106-113.

[12] Han X W, Zhao T J. Auto-k Dynamic Clustering Algorithm[J]. Asian Journal of Information Technology,2005,4(4):467-471.

[13] Deshpande M, Karypis G. Item Based Top N Recommendation Algorithms[J]. ACM Transactions on Information Systems, 2004,22(1): 143-177.
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