Compressed Video Aggregator: Content-driven Module for Efficient Micro-Video Recommendation
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917476731715584 |
|---|---|
| author | Xiao, Yang Chen, Huiyuan Deng, Kaiyuan Jiang, Chao Ling, Zinan Ye, Ruimeng Ma, Xiaolong Hui, Bo |
| author_facet | Xiao, Yang Chen, Huiyuan Deng, Kaiyuan Jiang, Chao Ling, Zinan Ye, Ruimeng Ma, Xiaolong Hui, Bo |
| contents | 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 on CLIP. Experiments on MicroLens and Short-Video show consistent gains with orders-of-magnitude reductions in training time and GPU memory, and re-selected frames can further enhance the performance of all methods, including CVA. Furthermore, we also discussed the impact of several scenarios involving erroneous titles on our method. Code will be released soon. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08810 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Compressed Video Aggregator: Content-driven Module for Efficient Micro-Video Recommendation Xiao, Yang Chen, Huiyuan Deng, Kaiyuan Jiang, Chao Ling, Zinan Ye, Ruimeng Ma, Xiaolong Hui, Bo Machine Learning Artificial Intelligence 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 on CLIP. Experiments on MicroLens and Short-Video show consistent gains with orders-of-magnitude reductions in training time and GPU memory, and re-selected frames can further enhance the performance of all methods, including CVA. Furthermore, we also discussed the impact of several scenarios involving erroneous titles on our method. Code will be released soon. |
| title | Compressed Video Aggregator: Content-driven Module for Efficient Micro-Video Recommendation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2605.08810 |