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

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.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2605.08810 [cs.LG]

(or arXiv:2605.08810v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.08810 Focus to learn more arXiv-issued DOI via DataCite

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