On-line recommendation method based on graph model and maximizing information retention
LI Yong-li1,2, WU Chong2, WANG Kun-sheng1
1. Institute of No.710, China Aerospace Science and Technology Corporation, Beijing 100037, China;
2. School of Management, Harbin Institute of Technology, Harbin 150001, China
Abstract：The development of E-commence calls for an effective and accurate recommendation method which not only convinces customers, but also accelerates circulation of commodities and promotes economic development. The existed recommendation methods paid attention to either the similarity of goods or that of customers, thus could not trade off the two aspects of information and make full use of them. In view of the above, this paper proposed recommendation method on the basis of graph model, which synthesized the similarity of customers and goods. The method built a comprehensive assessment model able to be transformed into its equivalent evaluation matrix and established an algorithm based on the above evaluation matrix with the aim of maximizing the retention of information. What’s more, this paper compared it with the benchmark methods. As a result, the numerical experiments show that the method has short-time calculations, high accuracy and is proper for real-time online recommendation.
李永立, 吴冲, 王崑声. 基于图论和信息最大化保留的在线推荐方法[J]. 系统工程理论与实践, 2011, 31(9): 1718-1725.
LI Yong-li, WU Chong, WANG Kun-sheng. On-line recommendation method based on graph model and maximizing information retention. Systems Engineering - Theory & Practice, 2011, 31(9): 1718-1725.
[1] Sulin B, Paul A P. Evidence of the effect of trust building technology in electronic markets: Price premiums and buyer behavior[J]. MIS Quarterly, 2002, 26(3): 243-268.[2] Paul A P, David G. Building effective online marketplaces with institution-based trust[J]. Information Systems Research, 2004, 15(1): 37-59.[3] Paul A P, Liang H, Xue Y. Understanding and mitigating uncertainty in online exchange relationships: A principal-agent perspective[J]. MIS Quarterly, 2007, 31(1): 105-136.[4] Park D H, Kim S. The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews[J]. Electronic Commerce Research and Applications, 2008, 7(4): 399-410.[5] Sarwar B M, Karypis G, Konstan J A, et al. Item-based collaborative filtering recommendation algorithms[C]//Proceedings of the 10th International World Wide Web Conference, 2001: 285-295.[6] Jonathan L H, Joseph A K, Loren G T, et al. Evaluating collaborative filtering recommender systems[C]//ACM Transactions on Information Systems, 2004, 22(1): 5-53.[7] Pan X, Deng G S, Liu J G. Information filtering via improved similarity definition[J]. Chinese Physical Letters, 2010, 27(6): 068903.[8] Wang J, Stephen R, Arjen P D V, et al. Probabilistic relevance ranking for collaborative filtering[J]. Information Retrieval, 2007, 11(6): 477-497.[9] Azene Z, Anthony F N. Representation, similarity measures and aggregation methods using fuzzy sets for contentbased recommender systems[J]. Fuzzy Sets and Systems, 2009, 160(1): 76-94.[10] Asela G, Guy S. A survey of accuracy evaluation metrics of recommendation tasks[J]. The Journal of Machine Learning Research, 2009, 10(12): 2935-2962.[11] Panagiotis S, Alexandros N, Apostolos N P, et al. Nearest-balusters’ collaborative filtering based on constant and coherent values[J]. Information Retrieval, 2008, 11(1): 51-75.[12] Liu R R, Jia C X, Zhou T, et al. Personal recommendation via modified collaborative filtering[J]. Physica A: Statistical Mechanics and Its Applications, 2009, 388(4): 462-468.[13] Zhang Z K, Zhou T, Zhang Y C. Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs[J]. Physica A: Statistical Mechanics and Its Applications, 2010, 389(1): 179-186.[14] Liu J G, Zhou T, Wang B H, et al. Degree correlation of bipartite network on personalized recommendation[J]. International Journal of Modern Physics C, 2010, 21(1): 137-147.[15] 袁玉波. 数据挖掘与最优化技术及其应用[M]. 北京: 科学出版社, 2007. Yuan Y B. Data Mining & Optimization Technology and Its Applications[M]. Beijing: Science Press, 2007.[16] David G L. Introduction to Dynamic Systems — Theory, Models and Applications[M]. 袁天鑫, 黄午阳,译. 上海: 上海科学技术文献出版社, 1985.
2011, Vol. 31 
