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

Duen-Ren Liu*, Ya Yueh Shih

*Corresponding author for this work

Research output: Contribution to journalArticle

91 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)181-191
Number of pages11
JournalJournal of Systems and Software
Volume77
Issue number2
DOIs
StatePublished - 1 Aug 2005

Keywords

  • Collaborative filtering
  • Customer lifetime value (CLV)
  • Data mining
  • Product recommendation
  • Recommender system

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