Mining changes in customer buying behavior for collaborative recommendations

被引:45
作者
Cho, YB [1 ]
Cho, YH
Kim, SH
机构
[1] Far E Univ, Dept eBusiness, 5 San Wangjang, Eumsung 369851, Chungbuk, South Korea
[2] Kookmin Univ, Sch eBusiness, Sungbuk Gu, Seoul 136702, South Korea
[3] Korea Adv Inst Sci & Technol, Grad Sch Management, Seoul 130012, South Korea
关键词
recommender systems; purchase sequence; collaborative filtering; SOM; association rules;
D O I
10.1016/j.eswa.2004.10.015
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The preferences of customers change over time. However, existing collaborative filtering (CF) systems are static, since they only incorporate information regarding whether a customer buys a product during a certain period and do not make use of the purchase sequences of customers. Therefore, the quality of the recommendations of the typical CF could be improved through the use of information on such sequences. In this study, we propose a new methodology for enhancing the quality of CF recommendation that uses customer purchase sequences. The proposed methodology is applied to a large department store in Korea and compared to existing CF techniques. Various experiments using real-world data demonstrate that the proposed methodology provides higher quality recommendations than do typical CF techniques, with better performance, especially with regard to heavy users. (C) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码:359 / 369
页数:11
相关论文
共 20 条
[1]  
Agrawal R., 1993, SIGMOD Record, V22, P207, DOI 10.1145/170036.170072
[2]   Fab: Content-based, collaborative recommendation [J].
Balabanovic, M ;
Shoham, Y .
COMMUNICATIONS OF THE ACM, 1997, 40 (03) :66-72
[3]  
BASU C, 1998, P 1998 WORKSH REC SY, P11
[4]  
BILLSUS D, 1998, P 15 INT C MACH LEAR, P46
[5]   Goal-oriented sequential pattern for network banking chum analysis [J].
Chiang, DA ;
Wang, YF ;
Lee, SL ;
Lin, CJ .
EXPERT SYSTEMS WITH APPLICATIONS, 2003, 25 (03) :293-302
[6]   Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce [J].
Cho, YH ;
Kim, JK .
EXPERT SYSTEMS WITH APPLICATIONS, 2004, 26 (02) :233-246
[7]   A personalized recommender system based on web usage mining and decision tree induction [J].
Cho, YH ;
Kim, JK ;
Kim, SH .
EXPERT SYSTEMS WITH APPLICATIONS, 2002, 23 (03) :329-342
[8]   TESTS OF EQUALITY BETWEEN SETS OF COEFFICIENTS IN 2 LINEAR REGRESSIONS [J].
CHOW, GC .
ECONOMETRICA, 1960, 28 (03) :591-605
[9]  
HILL W, 1995, P CHI 95
[10]   The self-organizing map [J].
Kohonen, T .
NEUROCOMPUTING, 1998, 21 (1-3) :1-6