Integrating AHP and data mining for product recommendation based on customer lifetime value

Duen-Ren Liu*, Ya Yueh Shih

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

295 Scopus citations


Product recommendation is a business activity that is critical in attracting customers. Accordingly, improving the quality of a recommendation to fulfill customers' needs is important in fiercely competitive environments. Although various recommender systems have been proposed, few have addressed the lifetime value of a customer to a firm. Generally, customer lifetime value (CLV) is evaluated in terms of recency, frequency, monetary (RFM) variables. However, the relative importance among them varies with the characteristics of the product and industry. We developed a novel product recommendation methodology that combined group decision-making and data mining techniques. The analytic hierarchy process (AHP) was applied to determine the relative weights of RFM variables in evaluating customer lifetime value or loyalty. Clustering techniques were then employed to group customers according to the weighted RFM value. Finally, an association rule mining approach was implemented to provide product recommendations to each customer group. The experimental results demonstrated that the approach outperformed one with equally weighted RFM and a typical collaborative filtering (CF) method.

Original languageEnglish
Pages (from-to)387-400
Number of pages14
JournalInformation and Management
Issue number3
StatePublished - 1 Mar 2005


  • Analytic hierarchy process (AHP)
  • Association rule mining
  • Clustering
  • Collaborative filtering
  • Customer lifetime value
  • Marketing
  • Recommendation


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