Mining changes in customer buying behavior for collaborative recommendations

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.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2005-02
Language
ENG
Keywords

SYSTEMS

Citation

EXPERT SYSTEMS WITH APPLICATIONS, v.28, no.2, pp.359 - 369

ISSN
0957-4174
DOI
10.1016/j.eswa.2004.10.015
URI
http://hdl.handle.net/10203/4624
Appears in Collection
KSIM-Journal Papers(저널논문)
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