Relative Advantage Debiasing for Watch-Time Prediction in Short-Video Recommendation
This addresses biased recommendations in short-video platforms, which can distort user preferences and reduce satisfaction, representing an incremental improvement over existing methods.
The paper tackles the problem of biased watch-time predictions in short-video recommendation due to confounding factors like video duration and popularity, proposing a relative advantage debiasing framework that corrects watch times using reference distributions and distributional embeddings, resulting in significant improvements in recommendation accuracy and robustness in offline and online experiments.
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.