  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.
