Compressed Video Aggregator: Content-driven Module for Efficient Micro-Video Recommendation
📰 ArXiv cs.AI
Learn how to build a Compressed Video Aggregator for efficient micro-video recommendation using content-driven modules
Action Steps
- Build a Compressed Video Aggregator module using VFM embeddings
- Aggregate frozen VFM embeddings to produce compact video embeddings
- Use latent reasoning without cross-attention projection to reduce computational complexity
- Configure the module to decouple video information from preference learning
- Test the CVA module on a benchmark dataset to evaluate its performance
Who Needs to Know This
Machine learning engineers and researchers working on video recommendation systems can benefit from this module to improve efficiency and accuracy
Key Insight
💡 Decoupling video information from preference learning can lead to more efficient and accurate video recommendation
Share This
📹 Improve micro-video recommendation with Compressed Video Aggregator! 🤖
Full Article
Title: Compressed Video Aggregator: Content-driven Module for Efficient Micro-Video Recommendation
Abstract:
arXiv:2605.08810v1 Announce Type: cross Abstract: We propose Compressed Video Aggregator (CVA), a lightweight micro-video recommendation module that decouples video information from preference learning. It aggregates frozen VFM embeddings, and uses latent reasoning without cross-attention projection, producing compact video embeddings for recommenders. Due to the redundancy in the frame count of the original benchmark and its overly coarse sampling, we used titles to re-select key frames based o
Abstract:
arXiv:2605.08810v1 Announce Type: cross Abstract: We propose Compressed Video Aggregator (CVA), a lightweight micro-video recommendation module that decouples video information from preference learning. It aggregates frozen VFM embeddings, and uses latent reasoning without cross-attention projection, producing compact video embeddings for recommenders. Due to the redundancy in the frame count of the original benchmark and its overly coarse sampling, we used titles to re-select key frames based o
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