Maximizing profit using recommender systems

Traditional recommendation systems make recommendations based solely on the customer’s past purchases, product ratings and demographic data without considering the profitability of the items being recommended. In this work we consider the question of how a vendor can directly incorporate the protability of items into its recommendation system so as to maximize expected prot while still providing accurate recommendations. Our approach uses the output of any traditional recommender system and adjusts it according to item protability. Our approach is parametrized so the vendor can control the amount of deviation between the recommendation incorporating prots and the traditional recommendation. We study our approach under two settings and show that it can achieve signicantly more prot than traditional recommendations.