Research on Collaborative Filtering Recommendation Algorithm by Fusing Social Network
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 Chengxian College, Nanjing 210088, China;
3. Information Science Department, Pennsylvania State University, Malvern 19355, USA
Abstract：Aiming at data sparsity and malicious behavior in traditional collaborative filtering algorithm, this paper presents a new algorithm of collaborative filtering based on social network. Depending on social network information, the algorithm integrates user’s trust and preference in order to find the nearest neighbors of the target user, which the algorithm uses to compute weight of neighbors and to form item recommendation. Experimental results show that the algorithm can alleviate the sparsity and malicious behaviors problems and achieve a better prediction accuracy than traditional collaborative filtering algorithms.
俞琰, 邱广华. 融合社会网络的协同过滤推荐算法研究[J]. 现代图书情报技术, 2012, 28(6): 54-59.
Yu Yan, Qiu Guanghua. Research on Collaborative Filtering Recommendation Algorithm by Fusing Social Network. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2012.06.09.
[1] Das A, Datar M, Garg A, et al. Google News Personalization: Scalable Online Collaborative Filtering[C]. In: Proceedings of the 16th International World Wide Web Conference. New York: ACM Press, 2007: 272-280.[2] Linden G, Smith B, York J. Amazon.com Recommendation: Item-to-Item Collaborative Filtering [J]. IEEE Internet Computing, 2003, 7(1):76-80.[3] Su X Y, Khoshgoftaar T M. A Survey of Collaborative Filtering Techniques[J]. Advances in Artificial Intelligence, 2009.[2011-12-08]. http://www.hindawi.com/journals/aai/2009/421425/.[4] Massa P, Avesani P. Trust-aware Recommender Systems[C]. In: Proceedings of the 2007 ACM Conference on Recommender Systems. New York: ACM Press, 2007:17-24.[5] Granovetter M S. The Strength of Weak Ties[J]. American Journal of Sociology, 1973, 78(6):1360-1380.[6] Kautz H, Selman B, Shah M. ReferralWeb: Combining Social Networks and Collaborative Filtering Communications of the ACM[J]. Communications of the ACM, 1997, 40(3):63-65.[7] Golbeck J. Generating Predictive Movie Recommendations from Trust in Social Networks[C]. In: Proceedings of the 4th International Conference on Trust Management(iTrust2006). Berlin,Heidelberg: Springer-Verlag, 2006:93-104.[8] Ziegler C N, Lausen G. Analyzing Correlation Between Trust and User Similarity in Online Communities[C]. In: Proceedings of the 2nd International Conference on Trust Management (iTrust 2004). Berlin,Heidelberg: Springer-Verlag,2004:251-265.[9] Avesani P, Massa P, Tiella R. A Trust-enhanced Recommender System Application: Moleskiing[C].In: Proceedings of the 2005 ACM Symposium on Applied Computing(SAC’05). New York: ACM Press, 2005:1589-1593.[10] Massa P, Avesani P. Trust-aware Recommender Systems[C]. In: Proceedings of the 2007 ACM Conference on Recommender Systems(RecSys’07). New York: ACM Press, 2007:17-24.[11] Vozalis E, Margaritis K G. Analysis of Recommender Systems’ Algorithms [C]. In: Proceedings of the 6th Hellenic European Conference on Computer Mathematics and Its Applications (HERCMA’2003), Athens, Greece. 2003:1-14.[12] Victor P, De Cock M, Cornelis C. Trust and Recommendations[A].//Ricci F,Rokach L, Shapira B, et al.Recommender Systems Handbook[M].Springer,2011:645-675.[13] Pan J Y, Yang H J, Faloutsos C, et al. GCap: Graph-based Automatic Image Captioning[C].In: Proceedings of the 2004 Computer Vision and Pattern Recognition Workshop(CVPRW ’04), Washington DC,USA. USA:IEEE CPS, 2004.[14] Urban J, Jose J M. Adaptive Image Retrieval Using a Graph Model for Semantic Feature Integration[C]. In: Proceedings of the 8th ACM International Workshop on Multimedia Information Retrieval. USA: ACM Press, 2006:117-126.[15] Fouss F, Pirotte A, Renders J M, et al. Random-walk Computation of Similarities Between Nodes of a Graph with Application to Collaborative Recommendation[J]. Knowledge and Data Engineering, 2007, 19(3):355-369.[16] 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.)[17] 俞琰, 邱广华. 用户兴趣变化感知的重启动随机游走推荐算法研究[J]. 现代图书情报技术 , 2012(4):48-53. (Yu Yan, Qiu Guanghua. Research on User Interest Shift Aware Random Walk with Restart Recommendation Algorithm[J]. New Technology of Library and Information Service, 2012(4):48-53.)[18] 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). Washington, DC, USA:IEEE Computer Society,2006:613-622.[19] De K J, Liekens A, Goethals B. GauSo: Graph Base Music Recommendation in a Social Bookmarking Service[C]. In: Proceedings of the 10th International Symposium on Advances in Intelligent Data Analysis X. New York: Spring-Verlag, 2011:138-149.[20] Boyd D. Friends, Friendsters, and MySpace Top 8: Writing Community into Being on Social Network Sites[J]. First Monday, 2006, 11(12):1-19.[21] Gross R, Acquisti A. Information Revelation and Privacy in Online Social Networks[C]. In: Proceedings of the 2005 ACM Workshop on Privacy in the Electronic Society. USA: ACM Press, 2005:71-80.
