Name
Title | ||
|---|---|---|
Content-Based Video Relevance Prediction with Multi-view Multi-level Deep Interest Network |
Abstract | ||
|---|---|---|
This paper presents our solution for the Hulu Content-Based Video Relevance Prediction (CBVRP) challenge, which focuses on cold-start videos as candidates. The keys to success of this prediction scenario are to learn effective user and video representations. To this end, we develop a multi-view multi-level deep interest network (MMDIN), which involves a multi-level deep interest network to learn user and video representations in a single-view, and a late fusion technique to integrate their multi-view representations corresponding to different types of video features. Through the above manner, the cold-start video prediction could be handled well with representations through their past interaction behaviors with videos and video representations based on their multiple types of content profiles.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3343031.3356068 | Proceedings of the 27th ACM International Conference on Multimedia |
Keywords | Field | DocType |
content-based video recommendation, deep learning, sequence modeling | Computer science,Relevance prediction,Artificial intelligence,Sequence modeling,Deep learning,Multimedia,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-6889-6 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zeyuan Chen | 1 | 6 | 4.83 |
| Kai Xu | 2 | 0 | 0.34 |
| Wei Zhang | 3 | 37 | 4.69 |