Relative Advantage Debiasing for Watch-Time Prediction in Short-Video Recommendation

TL;DR

This paper introduces a relative advantage debiasing framework for watch-time prediction in short-video recommendation, improving accuracy by correcting watch times for confounding factors using a novel two-stage approach and distributional embeddings.

Contribution

It proposes a new debiasing method that adjusts watch time signals through reference distributions, enhancing recommendation quality and robustness.

Findings

Significant improvement in recommendation accuracy.

Enhanced robustness against confounding factors.

Effective distributional embeddings for quantile parameterization.

Abstract

Watch time is widely used as a proxy for user satisfaction in video recommendation platforms. However, raw watch times are influenced by confounding factors such as video duration, popularity, and individual user behaviors, potentially distorting preference signals and resulting in biased recommendation models. We propose a novel relative advantage debiasing framework that corrects watch time by comparing it to empirically derived reference distributions conditioned on user and item groups. This approach yields a quantile-based preference signal and introduces a two-stage architecture that explicitly separates distribution estimation from preference learning. Additionally, we present distributional embeddings to efficiently parameterize watch-time quantiles without requiring online sampling or storage of historical data. Both offline and online experiments demonstrate significant improvements in recommendation accuracy and robustness compared to existing baseline methods.