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AI Research
arXiv CS.AI·Jun 10·5 min read
Atomic Intent Reasoning: Bringing LLM Semantics to Industrial Cross-Domain Recommendations
TL;DR
Cross-domain recommendation systems struggle with semantic gaps and noisy user behavior data. The authors developed Atomic Intent Reasoning (AIR), an LLM-driven framework that enhances recommendation accuracy by processing user intent offline.
✦ Why It Matters
Engineers can implement AIR to improve recommendation system efficiency and effectiveness in e-commerce applications.
Key Takeaways
- Utilize AIR for efficient cross-domain recommendations.
- Leverage offline LLM inference to enhance speed.
- Focus on dynamic user intent representation construction.
- Test improvements in real-world e-commerce scenarios.
Full Summary
Cross-domain recommendation is crucial for platforms that connect content with e-commerce, as it helps predict user purchasing intent based on their interactions. However, challenges arise due to significant semantic differences between domains and the noisy nature of user behavior data.
To tackle these issues, the authors introduced Atomic Intent Reasoning (AIR), which leverages large language models (LLMs) for semantic understanding while performing inference offline. This approach allows for dynamic construction of user intent representations during online operations, resulting in a remarkable 400-fold increase in inference speed.
Experimental results on various public datasets showed that AIR outperformed existing methods in cross-domain recommendation tasks. Additionally, large-scale A/B testing in Kuaishou E-commerce demonstrated a 3.446% increase in GMV, confirming the framework's effectiveness in real-world applications.
These findings suggest that AIR can significantly enhance the performance of recommendation systems in industrial settings.