Learning User Interests via Reasoning and Distillation for Cross-Domain News Recommendation
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915800402624512 |
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| author | Zhu, Mengdan Zhao, Yufan Di, Tao Yan, Yulan Zhao, Liang |
| author_facet | Zhu, Mengdan Zhao, Yufan Di, Tao Yan, Yulan Zhao, Liang |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_15005 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning User Interests via Reasoning and Distillation for Cross-Domain News Recommendation Zhu, Mengdan Zhao, Yufan Di, Tao Yan, Yulan Zhao, Liang Computation and Language Information Retrieval 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. |
| title | Learning User Interests via Reasoning and Distillation for Cross-Domain News Recommendation |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2602.15005 |