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arXiv · 2308.14276

Alleviating Video-Length Effect for Micro-video Recommendation

Yuhan Quan·Jingtao Ding·Chen Gao·Nian Li·Lingling Yi·Depeng Jin·Yong Li
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Abstract

Micro-videos platforms such as TikTok are extremely popular nowadays. One important feature is that users no longer select interested videos from a set, instead they either watch the recommended video or skip to the next one. As a result, the time length of users' watching behavior becomes the most important signal for identifying preferences. However, our empirical data analysis has shown a video-length effect that long videos are easier to receive a higher value of average view time, thus adopting such view-time labels for measuring user preferences can easily induce a biased model that favors the longer videos. In this paper, we propose a Video Length Debiasing Recommendation (VLDRec) method to alleviate such an effect for micro-video recommendation. VLDRec designs the data labeling approach and the sample generation module that better capture user preferences in a view-time oriented manner. It further leverages the multi-task learning technique to jointly optimize the above samples with original biased ones. Extensive experiments show that VLDRec can improve the users' view time by 1.81% and 11.32% on two real-world datasets, given a recommendation list of a fixed overall video length, compared with the best baseline method. Moreover, VLDRec is also more effective in matching users' interests in terms of the video content.

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Yuhan Quan, Jingtao Ding, Chen Gao, Nian Li, Lingling Yi, Depeng Jin, Yong Li. 2023-08-28. Alleviating Video-Length Effect for Micro-video Recommendation. https://arxiv.org/abs/2308.14276

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