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.

翻译：诸如TikTok等微视频平台如今极为流行。一个重要特征在于用户不再从一组视频中选择感兴趣的内容，而是要么观看推荐视频，要么跳过至下一个。因此，用户观看行为的时间长度成为识别偏好的最重要信号。然而，我们的实证数据分析揭示了视频长度效应：长视频更容易获得较高的平均观看时长，因而采用此类观看时长标签来衡量用户偏好容易导致模型产生偏向，倾向于推荐更长的视频。本文提出一种视频长度去偏推荐（VLDRec）方法，以缓解微视频推荐中的这种效应。VLDRec设计了数据标注方法和样本生成模块，能够以观看时长为导向更好地捕捉用户偏好，并进一步利用多任务学习技术将上述样本与原始有偏样本联合优化。大量实验表明，在固定总视频长度的推荐列表下，与最优基线方法相比，VLDRec在两个真实数据集上分别将用户观看时长提升了1.81%和11.32%。此外，VLDRec在根据视频内容匹配用户兴趣方面也更为有效。