Atomic Intent Reasoning: Bringing LLM Semantics to Industrial Cross-Domain Recommendations

The paper introduces Atomic Intent Reasoning (AIR), an LLM-driven framework for cross-domain recommendations that enhances industrial deployment by addressing the challenges of semantic gaps and noise in user behavior sequences. By offloading LLM inference to an offline phase and employing dynamic user intent representations, AIR achieves approximately 400 times faster inference while preserving semantic consistency. Experimental results and real-world A/B testing in Kuaishou E-commerce demonstrate state-of-the-art performance and a significant +3.446% increase in Gross Merchandise Value (GMV), highlighting its practical applicability for AI practitioners in recommendation systems.