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

Researchers propose Compressed Video Aggregator (CVA), a lightweight module that improves micro-video recommendation systems by decoupling video processing from preference learning. The method reduces training time and GPU memory by orders of magnitude while maintaining or improving performance through intelligent frame selection based on video titles.

The Compressed Video Aggregator addresses a fundamental efficiency problem in video recommendation systems, where processing high-frame-count videos creates computational bottlenecks. Traditional approaches treat all frames equally, leading to redundant computation and excessive memory consumption. CVA sidesteps this by leveraging frozen video foundation model embeddings and performing latent reasoning without expensive cross-attention mechanisms, achieving substantial computational gains while preserving recommendation quality.

This research emerges from broader industry trends toward efficient AI deployment. As recommendation systems scale to billions of users consuming vast video libraries, computational efficiency becomes critical infrastructure. The insight that video titles provide semantic guidance for frame selection reflects growing recognition that multimodal data contains complementary information often underutilized in standard architectures. Using CLIP-based title-guided frame selection represents a practical bridge between raw video content and meaningful visual features.

For practitioners building recommendation platforms, CVA's orders-of-magnitude reductions in training time and memory have direct operational impact. Faster training cycles enable more frequent model updates and A/B testing iterations. Reduced GPU memory requirements lower infrastructure costs, particularly important for smaller platforms competing against well-capitalized incumbents. The method's robustness—maintaining performance improvements even across different frame selections—suggests practical applicability despite real-world title quality variations.

The path forward involves validating generalization across diverse video categories and exploring whether other metadata signals could further optimize frame selection. The promised code release will be crucial for enabling adoption and community contribution.

• →CVA achieves orders-of-magnitude reductions in training time and GPU memory for video recommendation systems
• →Title-guided frame selection using CLIP improves performance across all tested recommendation methods
• →Decoupling video embedding from preference learning enables efficient latent reasoning without cross-attention projection
• →Method demonstrates robustness to erroneous titles, indicating practical viability in real-world applications
• →Computational efficiency gains directly reduce infrastructure costs and accelerate model iteration cycles