Maximizing profit using recommender systems

TL;DR

This paper proposes a method to incorporate item profitability into recommender systems, enabling vendors to maximize expected profit while maintaining recommendation accuracy, achieving about 22% more profit.

Contribution

It introduces a novel approach that adjusts traditional recommendations based on item profitability, balancing profit maximization with recommendation relevance.

Findings

Achieves approximately 22% higher profit than traditional recommenders.

Provides a parameterized framework for balancing profit and recommendation accuracy.

Validates the approach under two different settings.

Abstract

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.