Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time

TL;DR

This paper introduces a counterfactual watch time approach to mitigate duration bias in video recommendation systems, improving accuracy by better reflecting true user interests.

Contribution

It proposes the counterfactual watch time concept and a novel model (CWM) that estimates user interest more accurately by addressing duration bias.

Findings

CWM outperforms existing methods in accuracy on real datasets.

Counterfactual watch time reduces the impact of duration bias.

Online A/B tests show improved user engagement and recommendation quality.

Abstract

In video recommendation, an ongoing effort is to satisfy users' personalized information needs by leveraging their logged watch time. However, watch time prediction suffers from duration bias, hindering its ability to reflect users' interests accurately. Existing label-correction approaches attempt to uncover user interests through grouping and normalizing observed watch time according to video duration. Although effective to some extent, we found that these approaches regard completely played records (i.e., a user watches the entire video) as equally high interest, which deviates from what we observed on real datasets: users have varied explicit feedback proportion when completely playing videos. In this paper, we introduce the counterfactual watch time(CWT), the potential watch time a user would spend on the video if its duration is sufficiently long. Analysis shows that the duration bias is caused by the truncation of CWT due to the video duration limitation, which usually occurs on those completely played records. Besides, a Counterfactual Watch Model (CWM) is proposed, revealing that CWT equals the time users get the maximum benefit from video recommender systems. Moreover, a cost-based transform function is defined to transform the CWT into the estimation of user interest, and the model can be learned by optimizing a counterfactual likelihood function defined over observed user watch times. Extensive experiments on three real video recommendation datasets and online A/B testing demonstrated that CWM effectively enhanced video recommendation accuracy and counteracted the duration bias.