Joint Optimization of Profit and Relevance for Recommendation Systems in E-commerce

Authors: Raphael Louca, Moumita Bhattacharya, Diane Hu, Liangjie Hong

Presented at: RMSE Workshop at Recsys 2019 (Copenhagen, Denmark)

Abstract: Traditionally, recommender systems for e-commerce platforms are designed to optimize for relevance (e.g., purchase or click probability). Although such recommendations typically align with users' interests, they may not necessarily generate the highest profit for the platform. In this paper, we propose a novel revenue model which jointly optimizes both for probability of purchase and profit. The model is tested on a recommendation module at Etsy.com, a two-sided marketplace for buyers and sellers. Notably, optimizing for profit, in addition to purchase probability, benefits not only the platform but also the sellers. We show that the proposed model outperforms several baselines by increasing offline metrics associated with both relevance and profit.