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arXiv · 2605.08810

Compressed Video Aggregator: Content-driven Module for Efficient Micro-Video Recommendation

Yang Xiao·Huiyuan Chen·Kaiyuan Deng·Chao Jiang·Zinan Ling·Ruimeng Ye·Fei Wang·Xiaolong Ma·Bo Hui
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Abstract

We propose \textbf{Compressed Video Aggregator} (CVA), a lightweight micro-video recommendation module that decouples video information from preference learning. CVA first summarizes frozen VFM frame embeddings into a semantic-consensus anchor through masked mean pooling, projects this anchor into a compact latent space, and refines the projected representation with residual self-attention and feedforward blocks before producing a single video embedding for the recommender. 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.

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Yang Xiao, Huiyuan Chen, Kaiyuan Deng, Chao Jiang, Zinan Ling, Ruimeng Ye, Fei Wang, Xiaolong Ma, Bo Hui. 2026-07-28. Compressed Video Aggregator: Content-driven Module for Efficient Micro-Video Recommendation. https://arxiv.org/abs/2605.08810

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