Utilizing users' tipping points in E-commerce Recommender systems

Kailun Hu, Wynne Hsu, Mong-Li Lee

Abstract: Existing recommendation algorithms assume that users make their purchase decisions solely based on individual preferences, without regard to the type of users nor the products' maturity stages. Yet, extensive studies have shown that there are two types of users: innovators and imitators. Innovators tend to make purchase decisions based solely on their own preferences; whereas imitators' purchase decisions are often influenced by a product's stage of maturity. In this paper, we propose a framework that seamlessly incorporates the type of user and product maturity into existing recommendation algorithms. We apply Bass model to classify each user as either an innovator or imitator according to his/her previous purchase behavior. In addition, we introduce the concept of tipping point of a user. This tipping point refers to the point on the product maturity curve beyond which the user is likely to be more receptive to purchasing the product. We refine two widely-adopted recommendation algorithms to incorporate the effect of product maturity in relation to the user type. Experiment results on a real-world dataset obtained from an E-commerce website show that the proposed approach outperforms existing algorithms.