An Efficient Large Recommendation Model: Towards a Resource-Optimal Scaling Law

• URL: http://arxiv.org/abs/2502.09888v1
• Date: Fri, 14 Feb 2025 03:25:09 GMT
• Title: An Efficient Large Recommendation Model: Towards a Resource-Optimal Scaling Law
• Authors: Songpei Xu, Shijia Wang, Da Guo, Xianwen Guo, Qiang Xiao, Fangjian Li, Chuanjiang Luo,
• Abstract summary: Climber is a resource-efficient recommendation framework. It has been successfully deployed on Netease Cloud Music, one of China's largest music streaming platforms.
• Score: 2.688944054336062
• License:
• Abstract: The pursuit of scaling up recommendation models confronts intrinsic tensions between expanding model capacity and preserving computational tractability. While prior studies have explored scaling laws for recommendation systems, their resource-intensive paradigms -- often requiring tens of thousands of A100 GPU hours -- remain impractical for most industrial applications. This work addresses a critical gap: achieving sustainable model scaling under strict computational budgets. We propose Climber, a resource-efficient recommendation framework comprising two synergistic components: the ASTRO model architecture for algorithmic innovation and the TURBO acceleration framework for engineering optimization. ASTRO (Adaptive Scalable Transformer for RecOmmendation) adopts two core innovations: (1) multi-scale sequence partitioning that reduces attention complexity from O(n^2d) to O(n^2d/Nb) via hierarchical blocks, enabling more efficient scaling with sequence length; (2) dynamic temperature modulation that adaptively adjusts attention scores for multimodal distributions arising from inherent multi-scenario and multi-behavior interactions. Complemented by TURBO (Two-stage Unified Ranking with Batched Output), a co-designed acceleration framework integrating gradient-aware feature compression and memory-efficient Key-Value caching, Climber achieves 5.15x throughput gains without performance degradation. Comprehensive offline experiments on multiple datasets validate that Climber exhibits a more ideal scaling curve. To our knowledge, this is the first publicly documented framework where controlled model scaling drives continuous online metric growth (12.19% overall lift) without prohibitive resource costs. Climber has been successfully deployed on Netease Cloud Music, one of China's largest music streaming platforms, serving tens of millions of users daily.

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