收稿日期: 2016-02-28
修回日期: 2016-10-03
网络出版日期: 2017-05-30
基金资助
国家自然科学基金（No.61303096）；上海市自然科学基金（No.13ZR1454600）资助
Collaborative Filtering Recommendation Based on Double-Perspective of Users and Items
Received date: 2016-02-28
Revised date: 2016-10-03
Online published: 2017-05-30
程树林, 张博锋, 邹国兵 . 基于用户和项目的双视角协同过滤推荐方法[J]. 应用科学学报, 2017 , 35(3) : 326 -336 . DOI: 10.3969/j.issn.0255-8297.2017.03.006
Traditional collaborative fltering (CF) recommendation approach has a serious problems such as insufcient usage of single perspective information, unsatisfactory accuracy and sensitivity to data sparsity. To solve these problems, a CF recommendation method based on double-perspective of users and items is proposed by considering information of similar users and similar items. According to the dialectic principle, fusion similarity of items is given by combination of inner-factors and outer-factors of the item. This way, the item similarity and user similarity can be effectively measured. The measurement is robust against data sparsity in the approach of CF recommendation based on double-perspective of user and item. Several experiments are carried on benchmark datasets. The results show that the proposed CF recommendation method based on double-perspective of users and items outperforms several other typical CF approaches.
[1] Ricci F, Rokach L, Shapira B. Introduction to recommender systems handbook[M]. New York:Springer, 2011.
[2] Shi Y, Larson M, Hanjalic A. Collaborative fltering beyond the user-item matrix:a survey of the state of the art and future challenges[J]. ACM Computing Surveys, 2014, 47(1):1-45.
[3] Park D H, Kim H K, Choi I Y, Kim, J K. A literature review and classifcation of recommender systems research[J]. Expert Systems with Applications, 2012, 39(11):10059-10072.
[4] Linden G, Smith B, York J. Amazon.com recommendations:Item-to-item collaborative fltering[J]. IEEE on Internet Computing, 2003, 7(1):76-80
[5] 邓爱林,朱扬勇,施伯乐. 基于项目评分预测的协同过滤推荐算法[J]. 软件学报,2003, 14(9):1621-1628. Deng A L, Zhu Y Y, Shi B L. A collaborative fltering recommendation algorithm based on item rating prediction[J]. Journal of Software, 2003, 14(9):1621-1628(in Chinese).
[6] Ma H, King I, Lü M R. Effective missing data prediction for collaborative fltering[C]//Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2007:39-46.
[7] Lu Z, Dou Z, Lian J, Xie X, Yang Q. Content-based collaborative fltering for news topic recommendation[C]//29th AAAI Conference on Artifcial Intelligence, 2015:217-233.
[8] Song R P, Wang B, Huang G M, Liu Q D, Hu R J, Zhang R S. A hybrid recommender algorithm based on an improved similarity method[J]. Applied Mechanics and Materials, 2014, 475:978-982.
[9] Moin A, Ignat C L. Hybrid weighting schemes for collaborative fltering[D]. Paris:INRIA Nancy, 2014.
[10] Goldberg D, Nichols D, Oki B M, Terry D. Using collaborative fltering to weave an information tapestry[J]. Communications of the ACM, 1992, 35(12):61-70.
[11] Breese J S, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative fltering[C]//Proceedings of the Fourteenth Conference on Uncertainty in Artifcial Intelligence. Morgan Kaufmann Publishers Inc., 1998:43-52.
[12] Deshpande M, Karypis G. Item-based top-n recommendation algorithms[J]. ACM Transactions on Information Systems, 2004, 22(1):143-177.
[13] Rennie J D M, Srebro N. Fast maximum margin matrix factorization for collaborative prediction[C]//Proceedings of the 22nd International Conference on Machine Learning, 2005:713-719.
[14] Choi K, Suh Y. A new similarity function for selecting neighbors for each target item in collaborative fltering[J]. Knowledge-Based Systems, 2013, 37:146-153.
[15] Forsati R, Mahdavi M, Shamsfard M, Sarwat M. Matrix factorization with explicit trust and distrust relationships[J]. ArXiv:1408. 0325Ⅵ[cs.SI], 2014.
[16] Ghazanfar M A, Prugel A. The advantage of careful imputation sources in sparse dataenvironment of recommender systems:Generating improved svd-based recommendations[J]. Informatica, 2013, 37(1):61-92.
[17] Anand D, Bharadwaj K K. Pruning trust-distrust network via reliability and risk estimates for quality recommendations[J]. Social Network Analysis and Mining, 2013, 3(1):65-84.
[18] Agarwal V, Bharadwaj K K. A collaborative fltering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity[J]. Social Network Analysis and Mining, 2013, 3(3):359-379.
[19] Li W, Ye Z, Xin M, Jin Q. Social recommendation based on trust and influence in SNS environments[J]. Multimedia Tools and Applications, 2015:1-18.
[20] Odi?A, Tkal?i?, Tasi? J F, Košir A. Predicting and detecting the relevant contextual information in a movie-recommender system[J]. Interacting with Computers, 2013, 25(1):74-90.
[21] Shani G, Gunawardana A. Evaluating recommendation systems[M]. New York:Springer, 2011:257-297.
[22] Yang X, Guo Y, Liu Y, Steck H. A survey of collaborative fltering based social recommender systems[J]. Computer Communications, 2014, 41:1-10.
[23] Zenebe A, Zhou L, Norcio A F. User preferences discovery using fuzzy models[J]. Fuzzy Sets and Systems, 2010, 161(23):3044-3063.
[24] Wang J, De Vries A P, Reinders M J T. Unifying user-based and item-based collaborative fltering approaches by similarity fusion[C]//Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2006:501-508.
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