Large-Scale Content-Only Video Recommendation

Joonseok Lee, Sami Abu-El-Haija; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 987-995

Abstract


Traditional recommendation systems using collaborative filtering (CF) approaches work relatively well when the candidate videos are sufficiently popular. With the increase of user-created videos, however, recommending fresh videos gets more and more important, but pure CF-based systems may not perform well in such cold-start situation. In this paper, we model recommendation as a video content-based similarity learning problem, and learn deep video embeddings trained to predict video relationships identified by a co-watch-based system but using only visual and audial content. The system does not depend on availability on video meta-data, and can generalize to both popular and tail content, including new video uploads. We demonstrate performance of the proposed method in large-scale datasets, both quantitatively and qualitatively.

Related Material


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[bibtex]
@InProceedings{Lee_2017_ICCV,
author = {Lee, Joonseok and Abu-El-Haija, Sami},
title = {Large-Scale Content-Only Video Recommendation},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}
}