New perspectives on gray sheep behavior in e-commerce recommendations

Abstract

With the exponential rise in the size of data being generated, personalization based on recommender systems has become an important aspect of digital marketing strategy of E-Commerce companies. Recommender systems also help these companies in cross-selling, up-selling and to increase the customer loyalty. However, presence of certain users, known as gray sheep users, with eccentric taste, minimizes the overall efficiency of the recommender systems. Hence, their identification and removal from the computation system is critical for more efficient recommendations. This work presents psychographic models-based approaches for gray sheep user identification with improved performance. It also studies gray sheep behavior across different domains and contexts, apart from introducing the idea of gray sheep items.