mlsanityData to 7 Oct 2026

Learning User Interests via Reasoning and Distillation for Cross-Domain News Recommendation

arXiv 2602.15005 ↗First posted 16 Feb 2026cs.CL cs.IRcited by 0

On the map: LLM reasoning → LLM reasoning for recommendation

Abstract (arXiv)

News recommendation plays a critical role in online news platforms by helping users discover relevant content. Cross-domain news recommendation further requires inferring user's underlying information needs from heterogeneous signals that often extend beyond direct news consumption. A key challenge lies in moving beyond surface-level behaviors to capture deeper, reusable user interests while maintaining scalability in large-scale production systems. In this paper, we present a reinforcement learning framework that trains large language models to generate high-quality lists of interest-driven news search queries from cross-domain user signals. We formulate query-list generation as a policy optimization problem and employ GRPO with multiple reward signals. We systematically study two compute dimensions: inference-time sampling and model capacity, and empirically observe consistent improvements with increased compute that exhibit scaling-like behavior. Finally, we perform on-policy distillation to transfer the learned policy from a large, compute-intensive teacher to a compact student model suitable for scalable deployment. Extensive offline experiments, ablation studies and large-scale online A/B tests in a production news recommendation system demonstrate consistent gains in both interest modeling quality and downstream recommendation performance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning. Builds on 9 of its 27 references that are in the map. First posted on arXiv 16 Feb 2026.

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9 of 27 references.

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Same direction, newest first

  1. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond10 Oct

Other references · 8

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  3. 2025Understanding R1-Zero-Like Training: A Critical Perspectivecited by 1,276
  4. 2025Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Modelscited by 1,175
  5. 2015Session-based Recommendations with Recurrent Neural Networkscited by 685
  6. 2025Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domainscited by 328
  7. 2024Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendationscited by 184
  8. 2023CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendationcited by 99

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  1. 2019Deep Reinforcement Learning for Personalized Search Story Recommendation ↗
  2. 2024Generative News Recommendation
  3. 2026DiffuReason: Bridging Latent Reasoning and Generative Refinement for Sequential Recommendation
  4. 2024Aligning Large Language Models for Controllable Recommendations
  5. 2026RecoReward: Recommender-Guided Multimodal Description Generation for Recommendation
  6. 2026From Clues to Generation: Language-Guided Conditional Diffusion for Cross-Domain Recommendation
  7. 2026Generate to Explore, Select to Exploit: Aligning LLM-based Headline Generation with Personalized Recommendation
  8. 2020Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation