Cold Start Problem For Automated Live Video Comments

Hao Wu, François Pitie, Gareth Jones


Abstract
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. These comments have become a determining factor in the popularity of the videos. 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 the danmu comments 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 multi-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 state-of-the-art methods.
Anthology ID:
2021.maiworkshop-1.8
Volume:
Proceedings of the Third Workshop on Multimodal Artificial Intelligence
Month:
June
Year:
2021
Address:
Mexico City, Mexico
Venues:
NAACL | maiworkshop
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
54–62
Language:
URL:
https://aclanthology.org/2021.maiworkshop-1.8
DOI:
10.18653/v1/2021.maiworkshop-1.8
Bibkey:
Cite (ACL):
Hao Wu, François Pitie, and Gareth Jones. 2021. Cold Start Problem For Automated Live Video Comments. In Proceedings of the Third Workshop on Multimodal Artificial Intelligence, pages 54–62, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Cold Start Problem For Automated Live Video Comments (Wu et al., maiworkshop 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.maiworkshop-1.8.pdf