Development of a recommender system based on navigational and behavioral patterns of customers in e-commerce sites

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In this article, a novel CF (collaborative filtering)-based recommender system is developed for e-commerce sites. Unlike the conventional approach in which only binary purchase data are used, the proposed approach analyzes the data captured from the navigational and behavioral patterns of customers, estimates the preference levels of a customer for the products which are clicked but not purchased, and CF is conducted using the preference levels for making recommendations. This also compares with the existing works on clickstream data analysis in which the navigational and behavioral patterns of customers are analyzed for simple relationships with the target variable. The effectiveness of the proposed approach is assessed using an experimental e-commerce site. It is found among other things that the proposed approach outperforms the conventional approach in almost all cases considered. The proposed approach is versatile and can be applied to a variety of e-commerce sites as long as the navigational and behavioral patterns of customers can be captured. (C) 2004 Elsevier Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2005-02
Language
English
Article Type
Article
Citation

EXPERT SYSTEMS WITH APPLICATIONS, v.28, pp.381 - 393

ISSN
0957-4174
DOI
10.1016/j.eswa.2004.10.017
URI
http://hdl.handle.net/10203/14213
Appears in Collection
IE-Journal Papers(저널논문)
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