Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences

Duen-Ren Liu*, Ya Yueh Shih

*Corresponding author for this work

研究成果: Article同行評審

91 引文 斯高帕斯(Scopus)

摘要

Recommending products to attract customers and meet their needs is important in fiercely competitive environments. Recommender systems have emerged in e-commerce applications to support the recommendation of products. Recently, a weighted RFM-based method (WRFM-based method) has been proposed to provide recommendations based on customer lifetime value, including Recency, Frequency and Monetary. Preference-based collaborative filtering (CF) typically makes recommendations based on the similarities of customer preferences. This study proposes two hybrid methods that exploit the merits of the WRFM-based method and the preference-based CF method to improve the quality of recommendations. Experiments are conducted to evaluate the quality of recommendations provided by the proposed methods, using a data set concerning the hardware retail marketing. The experimental results indicate that the proposed hybrid methods outperform the WRFM-based method and the preference-based CF method.

原文English
頁(從 - 到)181-191
頁數11
期刊Journal of Systems and Software
77
發行號2
DOIs
出版狀態Published - 1 八月 2005

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