Integrating analytic hierarchy process and data mining for product recommendation based on customer lifetime value

Integrating analytic hierarchy process and data mining for product recommendation based on customer lifetime value

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Article ID: iaor20051255
Country: Netherlands
Volume: 42
Issue: 3
Start Page Number: 387
End Page Number: 400
Publication Date: Mar 2005
Journal: Information and Management
Authors: ,
Keywords: decision theory: multiple criteria, datamining, analytic hierarchy process
Abstract:

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.

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