Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations

Abstract

Recommender systems are seen as an effective tool to address information overload, but it is widely known that the presence of various biases makes direct training on large-scale observational data result in sub-optimal prediction performance. In contrast, unbiased ratings obtained from randomized controlled trials or A/B tests are considered to be the golden standard, but are costly and small in scale in reality. To exploit both types of data, recent works proposed to use unbiased ratings to correct the parameters of the propensity or imputation models trained on the biased dataset. However, the existing methods fail to obtain accurate predictions in the presence of unobserved confounding or model misspecification. In this paper, we propose a theoretically guaranteed model-agnostic balancing approach that can be applied to any existing debiasing method with the aim of combating unobserved confounding and model misspecification. The proposed approach makes full use of unbiased data by alternatively correcting model parameters learned with biased data, and adaptively learning balance coefficients of biased samples for further debiasing. Extensive real-world experiments are conducted along with the deployment of our proposal on four representative debiasing methods to demonstrate the effectiveness. CCS CONCEPTS• Information systems → Recommender systems.

LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting

“…To tackle these biases, three main research lines have emerged. The first line of research involves utilizing unbiased data to guide model learning [4,13,29], although acquiring such data can be expensive. The second line of research focuses on mitigating biases from a causal perspective, which can be categorized into intervention [8,24,40] and counterfactual methods [22,31,35].…”