Cold Start Problem For Automated Live Video Comments
Hao Wu, François Pitié, Gareth J. F. Jones · 2021
Live video comments, or "danmu", are an emerging feature on Asian online video platforms.Danmu are time-synchronous comments that are overlaid on a video playback.These comments uniquely enrich the experience and engagement of their users, and have become a determining factor in the popularity of videos on these platforms.Similar to the "cold start problem" in recommender systems, a video will only start to attract attention when sufficient danmu comments have been posted on it.We study this video cold start problem and examine how new comments can be generated automatically on less-commented videos.We propose to predict danmu comments to promote user engagement, by exploiting a multi-modal combination of the video visual content, subtitles, audio signals, and any surrounding comments (when they exist).Our method fuses these multiple modalities in a transformer network which is then trained for different comment density scenarios.We evaluate our proposed system through both a retrieval based evaluation method, as well as human judgement.Results show that our proposed system improves significantly over stateof-the-art methods.