Representation Matters When Learning From Biased Feedback in Recommendation

Abstract

The logged feedback for training recommender systems is usually subject to selection bias, which could not reflect real user preference. Thus, many efforts have been made to learn the de-biased recommender system from biased feedback. However, existing methods for dealing with selection bias are usually affected by the error of propensity weight estimation, have high variance, or assume access to uniform data, which is expensive to be collected in practice. In this work, we address these issues by proposing Learning De-biased Representations (LDR), a framework derived from the representation learning perspective. LDR bridges the gap between propensity weight estimation (WE) and unbiased weighted learning (WL) and provides an end-to-end solution that iteratively conducts WE and WL. We show LDR can effectively alleviate selection bias with bounded variance. We also perform theoretical analysis on the statistical properties of LDR, such as its bias, variance, and generalization performance. Extensive experiments on both semi-synthetic and real-world datasets demonstrate the effectiveness of LDR.