arXiv:2602.10455v2 Announce Type: replace-cross Abstract: Driven by scaling laws, recommender systems increasingly rely on larger-scale models to capture complex feature interactions and user behaviors, but this trend also leads to prohibitive training and inference costs. While long-sequence models can reuse user-side computation through KV Caching, such reuse is difficult in TokenMixer-based dense feature interaction architectures, where user and group features are deeply entangled and mixed-up across layers. In this work, we present User-Group Separation (UG-Sep), an industrial large-scale
The increasing scale of recommender systems and the prohibitive costs associated with their training and inference are driving innovation in efficiency techniques.
Efficient large recommendation models are critical for the economic viability and widespread adoption of AI-driven platforms, directly impacting operational costs and user experience.
This research outlines a method to significantly reduce computational costs for large recommendation models, potentially accelerating their development and deployment across various industries.
- · Large-scale AI platforms
- · Recommendation system providers
- · Cloud computing providers (reduced cost for clients)
- · E-commerce & content platforms
- · Inefficient model architectures
- · Companies unable to adapt to new efficiency standards
Reduced operational costs for AI-powered recommendation services.
Faster innovation cycles for advanced AI models as computational bottlenecks decrease.
Broader deployment of sophisticated AI recommender systems in sectors currently limited by compute costs.
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Read at arXiv cs.LG