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Feature Re-Learning with Data Augmentation for Content-based Video Recommendation
- Source :
- ACM Multimedia
- Publication Year :
- 2018
- Publisher :
- ACM, 2018.
-
Abstract
- 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.
- Subjects :
- business.industry
Computer science
Supervised learning
02 engineering and technology
Machine learning
computer.software_genre
Feature (computer vision)
020204 information systems
Content (measure theory)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
Baseline (configuration management)
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 26th ACM international conference on Multimedia
- Accession number :
- edsair.doi...........b050a8b54e8d2bdf6fcacd4ea883aa66