  Traditional recommendation systems make recommendations based solely on the
customer's past purchases, product ratings and demographic data without
considering the profitability the items being recommended. In this work we
study the question of how a vendor can directly incorporate the profitability
of items into its recommender so as to maximize its expected profit while still
providing accurate recommendations. Our approach uses the output of any
traditional recommender system and adjust them according to item
profitabilities. Our approach is parameterized so the vendor can control how
much the recommendation incorporating profits can deviate from the traditional
recommendation. We study our approach under two settings and show that it
achieves approximately 22% more profit than traditional recommendations.
