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.

翻译：在视频推荐中，持续的努力在于通过利用用户记录的观看时长来满足其个性化的信息需求。然而，观看时长预测受到时长偏差的影响，阻碍了其准确反映用户兴趣的能力。现有的标签校正方法试图通过根据视频时长对观察到的观看时长进行分组和归一化来揭示用户兴趣。尽管这些方法在一定程度上有效，但我们发现它们将完全播放记录（即用户观看了整个视频）视为同等的高兴趣，这与我们在真实数据集上观察到的情况不符：用户在完全播放视频时表现出不同的显式反馈比例。本文引入了反事实观看时长，即如果视频时长足够长，用户可能在该视频上花费的潜在观看时间。分析表明，时长偏差是由视频时长限制导致的反事实观看时长截断引起的，这通常发生在那些完全播放的记录上。此外，本文提出了一个反事实观看模型，该模型揭示反事实观看时长等于用户从视频推荐系统中获得最大收益的时间。进一步地，定义了一个基于成本的转换函数，将反事实观看时长转换为用户兴趣的估计，并且该模型可以通过优化基于观察到的用户观看时长定义的反事实似然函数来学习。在三个真实视频推荐数据集上的大量实验以及在线A/B测试表明，反事实观看模型有效提升了视频推荐的准确性并抵消了时长偏差。