Personalized pricing recommender system: multi-stage epsilon-greedy approach

Abstract

Many e-commerce sites use recommender systems, which suggest items that customers prefer. Though recommender systems have achieved great success, their potential is not yet fulfilled. One weakness of current systems is that the actions of the system toward customers are restricted to simply showing items. We propose a system that relaxes this restriction to offer price discounting as well as recommendations. The system can determine whether or not to offer price discounting for individual customers, and such a pricing scheme is called price personalization. We discuss how the introduction of price personalization improves the commercial viability of managing a recommender system, and thereby improving the customers' sense of the system's reliability. We then propose a method for adding price personalization to standard recommendation algorithms which utilize two types of customer data: preferential data and purchasing history. Based on the analysis of the experimental results, we reveal further issues in designing a personalized pricing recommender system.