Atomic Intent Reasoning: Bringing LLM Semantics to Industrial Cross-Domain Recommendations
Cross-domain recommendation is a core problem in content-to-e-commerce platforms. Its objective is to leverage user interactions with content to infer potential purchasing intent on the e-commerce side, thereby enhancing conversion rates and commercial value. However, in real industrial scenarios, cross-domain recommendation faces multiple challenges: significant semantic gaps exist between different domains, and user cross-domain behavior sequences are often massive in scale and rich in noise. Although large language models (LLMs) possess powerful semantic understanding and reasoning capabilities, their millisecond-level inference latency makes direct application in online recommendation...
| Authors | Zhuohang Jiang, Yuxin Chen, Shijie Wang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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