Feature Re-Learning with Data Augmentation for Content-based Video Recommendation

We are going to present our work on content-based video recommendation in the Multimedia Grand Challenge session of the forthcoming ACM Multimedia 2018 Conference at Seoul. Source code will be available shortly at https://github.com/danieljf24/cbvr.

This paper describes our solution for the Hulu Content-based Video Relevance Prediction Challenge. Noting the deficiency of the original features, we propose feature re-learning to improve video relevance prediction. To generate more training instances for supervised learning, we develop two data augmentation strategies, one for frame-level features and the other for video-level features. In addition, late fusion of multiple models is employed to further boost the performance. Evaluation conducted by the organizers shows that our best run outperforms the Hulu baseline, obtaining relative improvements of 26.2% and 30.2% on the TV-shows track and the Movies track, respectively, in terms of recall@100. The results clearly justify the effectiveness of the proposed solution.

Jianfeng Dong, Xirong Li, Chaoxi Xu, Gang Yang, Xun Wang (2018): Feature Re-Learning with Data Augmentation for Content-based Video Recommendation. ACM Multimedia, 2018, (Grand challenge paper).

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