Extended Collaborative Filtering Technique for Mitigating the Sparsity Problem
Keywords:
recommendation system, collaborative filtering, sparsity problem, similarity functionAbstract
Many online shopping malls have implemented personalized recommendation systems to improve customer retention in the age of high competition and information overload. Sellers make use of these recommendation systems to survive high competition and buyers utilize them to find proper product information for their own needs. However, transaction data of most online shopping malls prevent us from using collaborative filtering (CF) technique to recommend products, for the following two reasons: 1) explicit rating information is rarely available in the transaction data; 2) the sparsity problem usually occurs in the data, which makes it difficult to identify reliable neighbors, resulting in less effective recommendations. Therefore, this paper first suggests a means to derive implicit rating information from the transaction data of an online shopping mall and then proposes a new user similarity function to mitigate the sparsity problem. The new user similarity function computes the user similarity of two users if they rated similar items, while the user similarity function of traditional CF technique computes it only if they rated common items. Results from several experiments using an online shopping mall dataset in Korea demonstrate that our approach significantly outperforms the traditional CF technique.References
M. Pazzani, D. Billsus (1997); Learning and Revising User Profile: The Identification of Interesting Web Sites, Machine Learning, 27(3): 313-331. http://dx.doi.org/10.1023/A:1007369909943
T. Zhang, R. Agarwal, H.C. Lucas (2011); The Value of IT-Enabled Retailer Learning: Personalized Product Recommendations and Customer Store Loyalty in Electronic Markets, MIS Quarterly, 35(4): 859-881.
Y. Jing, H. Liu (2013); A Model for Collaborative Filtering Recommendation in E-Commerce Environment, International Journal of Computers Communications and Control, 8(4): 560- 570. http://dx.doi.org/10.15837/ijccc.2013.4.577
D.R. Liu, C.H. Lai, W.J. Lee (2009); A Hybrid of Sequential Rules and Collaborative Filtering for Product Recommendation, Information Sciences, 179(20): 3505-3519. http://dx.doi.org/10.1016/j.ins.2009.06.004
M. Balabanovic, Y. Shoham (1998); Content-Based, Collaborative Recommendation, Communications of the ACM, 40(3): 66-72. http://dx.doi.org/10.1145/245108.245124
K. Lang (1995); NewsWeeder: Learning to Filter Netnews, Pro. of the 12th Int. Conference on Machine Learning.
U. Shardanand, P. Maes (1995); Social Information Filtering Algorithms for Automating "Word of Mouth", Pro. of the SIGCHI Conference on Human Factors in Computing Systems.
G. Adomavicius, A. Tuzhilin (2005); Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, IEEE Transactions on Knowledge and Data Engineering, 17(6): 734-749. http://dx.doi.org/10.1109/TKDE.2005.99
D. Billsus, M.J. Pazzani (1998); Learning Collaborative Information Filters, Pro. of the 15th Int. Conference on Machine Learning.
V. Formoso, D. Fernandez, F. Cacheda, V. Carneiro (2012); Using Profile Expansion Techniques to Alleviate the New User Problem, Information Processing and Management, 49(3): 659-672. http://dx.doi.org/10.1016/j.ipm.2012.07.005
H.N. Kim, A.T. Ji, I. Ha, G.S. Jo (2010); Collaborative Filtering based on Collaborative Tagging for Enhancing the Quality of Recommendation, Electronic Commerce Research and Applications, 9(1): 73-83. http://dx.doi.org/10.1016/j.elerap.2009.08.004
T.Q. Lee, Y. Park, Y.T. Park (2008); A Time-based Approach to Effective Recommender Systems Using Implicit Feedback, Expert Systems with Applications, 34(4): 3055-3062. http://dx.doi.org/10.1016/j.eswa.2007.06.031
Q. Shambour, J. Lu (2011); A Hybrid Trust-Enhanced Collaborative Filtering Recommendation Approach for Personalized Government-to-Business e-Services, International Journal of Intelligent Systems, 26(9): 814-843. http://dx.doi.org/10.1002/int.20495
C.C. Aggarwal, C. Procopiuc, P.S. Yu (2002); Finding Localized Associations in Market Basket Data. IEEE Transactions on Knowledge and Data Engineering, 14(1): 51-62. http://dx.doi.org/10.1109/69.979972
C.L. Huang, W.L. Huang (2009); Handling Sequential Pattern Decay: Developing a Two- Stage Collaborative Recommendation System, Electronic Commerce Research and Applications, 8(3): 117-129. http://dx.doi.org/10.1016/j.elerap.2008.10.001
Y. Wang, W. Dai, Y. Yuan (2008); Website Browsing Aid: A Navigation Graph-based Recommendation System, Decision Support Systems, 45(3): 387-400. http://dx.doi.org/10.1016/j.dss.2007.05.006
D. Goldberg, D. Nichols, B.M. Oki, D. Terry (1992); Using Collaborative Filtering to Weave an Information Tapestry, Communications of the ACM, 35(12): 61-70. http://dx.doi.org/10.1145/138859.138867
P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, J. Riedl (1994); GroupLens: An Open Architecture for Collaborative Filtering of Netnews. Pro. of the 1994 ACM Conference on Computer Supported Cooperative Work.
G. Adomavicius, Y. Kwon (2007); New Recommendation Techniques for Multicriteria Rating Systems, IEEE Intelligent Systems, 22(3): 48-55.
I.S. Altingovde, O.N. Subakan, O. Ulusoy (2012); Cluster Searching Strategies for Collaborative Recommendation Systems, Information Processing and Management, 49(3): 688-697. http://dx.doi.org/10.1016/j.ipm.2012.07.008
K.W. Cheung, J.T. Kwok, M.H. Law, K.C. Tsui (2003); Mining Customer Product Ratings for Personalized Marketing, Decision Support Systems, 35(2): 231-243. http://dx.doi.org/10.1016/S0167-9236(02)00108-2
K. Goldberg, T. Roeder, D. Gupta, C. Perkins (2001); Eigentaste: A Constant Time Collaborative Filtering Algorithm, Information Retrieval, 4(2): 133-151. http://dx.doi.org/10.1023/A:1011419012209
Y. Koren (2010); Factor in the Neighbors: Scalable and Accurate Collaborative Filtering, ACM Transactions on Knowledge Discovery from Data, 4(1): 1-24. http://dx.doi.org/10.1145/1644873.1644874
R. Salakhutdinov, N. Srebro (2010); Collaborative Filtering in a Non-UniformWorld: Learning with the Weighted Trace Norm, arXiv:1002.2780v1, 1-9.
D. Joaquin, I. Naohiro (1999); Memory-based Weighted-Majority Prediction. Pro. of ACM SIGIR' 99 Workshop on Recommender Systems: Algorithms and Evaluation.
J. Lee, S. Lee, H. Kim (2011); An Probabilistic Approach to Semantic Collaborative Filtering Using World Knowledge, Journal of Information Science, 37(1): 49-66. http://dx.doi.org/10.1177/0165551510392318
J.M. Yang, K.F. Li, D.F. Zhang (2009); Recommendation based on Rational Inferences in Collaborative Filtering, Knowledge-Based Systems, 22(1): 105-114. http://dx.doi.org/10.1016/j.knosys.2008.07.004
Z. Liu, W. Qu, H. Li, C. Xie (2010); A Hybrid Collaborative Filtering Recommendation Mechanism for P2P Networks, Future Generation Computer Systems, 26(8): 1409-1417. http://dx.doi.org/10.1016/j.future.2010.04.002
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