Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time
Authors: 
Haiyuan Zhao, Guohao Cai, Jieming Zhu, Zhenhua Dong, 
Jun Xu, Ji-Rong WenAuthors Info & Claims
Haiyuan Zhao, Guohao Cai, Jieming Zhu, Zhenhua Dong, Jun Xu, Ji-Rong WenAuthors Info & ClaimsPages 4455 - 4466
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.
Supplemental Material
MP4 File - CWM_promo_video
In video recommendation, satisfying users' personalized needs often relies on logged watch time, but this method is hampered by duration bias, inaccurately reflecting user interests. Existing methods normalize watch time based on video duration but mistakenly equate fully watched videos with high interest, ignoring varied user feedback. We introduce Counterfactual Watch Time (CWT), representing the potential watch time if videos were longer. Our analysis shows duration bias stems from CWT truncation, especially in fully watched videos. We propose the Counterfactual Watch Model (CWM), transforming CWT into user interest estimates via a cost-based function. Extensive experiments on three datasets and online A/B tests confirm that CWM significantly improves recommendation accuracy by counteracting duration bias.
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Index Terms
- Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time
Recommendations
Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation
RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsIn the video recommendation, watch time is commonly adopted as an indicator of user interest. However, watch time is not only influenced by the matching of users’ interests but also by other factors, such as duration bias and noisy watching. Duration ...
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