Personalized product recommendation based on customer value hierarchy

Abstract

Recommender systems apply statistical and knowledge discovery techniques to the problem of making product recommendations during a live customer interaction and they are achieving widespread success in e-commerce nowadays. In this article, we present a novel product recommendation approach, which involves customer value hierarchy model into traditional recommender systems. This approach is divided into two phases. Product categories are recommended using the collaborative filtering algorithm in the first phase. In phase II, product items are recommended, based on customer value hierarchy model, to customers whose purchasing goals are met by these products' attributes. In contrast to traditional approaches, which provide recommendation by the opinions of customers with the similar purchasing behavior, the proposed approaches root out customer's purchasing motivation and maximize customer satisfaction.