Alleviating Matching Bias in Marketing Recommendations

Abstract

In marketing recommendations, the campaign organizers will distribute coupons to users to encourage consumption. In general, a series of strategies are employed to interfere with the coupon distribution process, leading to a growing imbalance between user-coupon interactions, resulting in a bias in the estimation of conversion probabilities. We refer to the estimation bias as the matching bias. In this paper, we explore how to alleviate the matching bias from the causal-effect perspective. We regard the historical distributions of users and coupons over each other as confounders and characterize the matching bias as a confounding effect to reveal and eliminate the spurious correlations between user-coupon representations and conversion probabilities. Then we propose a new training paradigm named De-Matching Bias Recommendation (Dmbr) to remove the confounding effects during model training via the backdoor adjustment. We instantiate Dmbr on two representative models: Dnn and Mmoe, and conduct extensive offline and online experiments to demonstrate the effectiveness of our proposed paradigm.