Integrating rating-based collaborative filtering with customer lifetime value: New product recommendation technique

Article type: Research Article

Authors: Albadvi, Amir^{; *} | Shahbazi, Mohammad

Affiliations: Department of Industrial Engineering, Tarbiat Modares University, Jalal Alahmad Highway, P.O. Box 14115-143, Tehran, Iran

Correspondence: [*] Corresponding author. Tel.: +98 021 82883395; E-mail: Albadvi@Modares.ac.ir

Abstract: Recommender systems are changing from novelties used by a few E-commerce sites to serious business tools. They recommend products to customers based on their historical preferences. Through several recommendation techniques, Collaborative filtering (CF) is the most successful recommendation method which is widely used. Nowadays, customer lifetime value (CLV) is measured by RFM (Recency, Frequency, and Monetary) and weighted RFM-based method is used in product recommendation. In this paper, we present a product recommendation technique for online retail stores which employs CLV concept and integrates it with CF method to generate better quality recommendations. In this paper, CF is applied to customer ratings on products, which are collected implicitly by web usage mining approach. Product taxonomy is also used to segment products according to their categories and to reduce dimensions of computational space. The experimental results show that the proposed technique outperforms several other similar recommendation methods.

Keywords: Recommender system, collaborative filtering, customer lifetime value, product taxonomy

DOI: 10.3233/IDA-2010-0412

Journal: Intelligent Data Analysis, vol. 14, no. 1, pp. 143-155, 2010