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

arXiv: https://arxiv.org/abs/2606.10357 · HTML: https://mlsanity.com/paper/2606.10357/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning.
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Placed in

- LLM reasoning / [LLM reasoning for recommendation](https://mlsanity.com/lr/llm-reasoning-for-recommendation/)

## Builds on (most cited first, 13 of 13)

- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2105.03300/) (2021)
- [A Survey on Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2401.04971/) (2024)
- [mKG-RAG: Leveraging Multimodal Knowledge Graphs in Retrieval-Augmented Generation for Knowledge-intensive VQA](https://mlsanity.com/paper/2508.05318/) (2025)
- [Diffusion Generative Recommendation with Continuous Tokens](https://mlsanity.com/paper/2504.12007/) (2025)
- [Venus-DeFakerOne: Unified Fake Image Detection & Localization](https://mlsanity.com/paper/2605.14091/) (2026)
- [QA-Dragon: Query-Aware Dynamic RAG System for Knowledge-Intensive Visual Question Answering](https://mlsanity.com/paper/2508.05197/) (2025)
- [SUPERGLASSES: Benchmarking Vision Language Models as Intelligent Agents for AI Smart Glasses](https://mlsanity.com/paper/2602.22683/) (2026)
- [Stop Treating Collisions Equally: Qualification-Aware Semantic ID Learning for Recommendation at Industrial Scale](https://mlsanity.com/paper/2603.00632/) (2026)
- [Fundus-R1: Training a Fundus-Reading MLLM with Knowledge-Aware Reasoning on Public Data](https://mlsanity.com/paper/2604.08322/) (2026)
- [Beyond Static Collision Handling: Adaptive Semantic ID Learning for Multimodal Recommendation at Industrial Scale](https://mlsanity.com/paper/2604.23522/) (2026)

## Built on by (most cited first, 2 of 2)

- [A/B Agent: A Self-Evolving Agent for Strategy Iteration in Industrial A/B Testing](https://mlsanity.com/paper/2608.04625/) (2026)
- [AI Smart Glasses for Wearable Intelligence: From Egocentric Sensing to Agentic Personalization](https://mlsanity.com/paper/2609.19793/) (2026)

## Abstract (arXiv)

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 systems difficult. To address these issues, this paper introduces AIR (Atomic Intent Reasoning), an LLM-driven cross-domain recommendation framework designed for industrial-grade deployment. By migrating LLM inference to the offline phase and dynamically constructing user intent representations through efficient retrieval and composition during online operations, it achieves approximately 400* inference acceleration while maintaining semantic consistency. Experimental results across multiple public datasets demonstrate that our method achieves state-of-the-art performance in cross-domain recommendation tasks. Furthermore, large-scale online A/B testing conducted in Kuaishou E-commerce's real-world business scenarios shows that our approach delivers stable and significant improvements across multiple core business metrics, including a +3.446% increase in GMV, fully validating its effectiveness and practical value in industrial-scale recommendation systems.