Utilizing users tipping points in recommender systems

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.

In this thesis, we propose a framework that 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. 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 two real-world datasets show that the proposed approach outperforms existing algorithms.