  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.
