Product recommendation approaches: Collaborative filtering via customer lifetime value and customer demands

Recommender systems are techniques that allow companies to develop one-to-one marketing strategies and provide support in connecting with customers for e-commerce. There exist various recommendation techniques, including collaborative filtering (CF), content-based filtering, WRFM-based method, and hybrid methods. The CF method generally utilizes past purchasing preferences to determine recommendations to a target customer based on the opinions of other similar customers. The WRFM-based method makes recommendations based on weighted customer lifetime value - Recency, Frequency and Monetary. This work proposes to use customer demands derived from frequently purchased products in each industry as valuable information for making recommendations. Different from conventional CF techniques, this work uses extended preferences derived by combining customer demands and past purchasing preferences to identify similar customers. Accordingly, this work proposes several hybrid recommendation approaches that combine collaborative filtering, WRFM-based method, and extended preferences. The proposed approaches further utilize customer demands to adjust the ranking of recommended products to improve recommendation quality. The experimental results show that the proposed methods perform better than several other recommendation methods.