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

• URL: http://arxiv.org/abs/2602.15005v1
• Date: Mon, 16 Feb 2026 18:45:40 GMT
• Title: Learning User Interests via Reasoning and Distillation for Cross-Domain News Recommendation
• Authors: Mengdan Zhu, Yufan Zhao, Tao Di, Yulan Yan, Liang Zhao,
• Abstract summary: News recommendation plays a critical role in online news platforms by helping users discover relevant content.<n>Cross-domain news recommendation further requires inferring user's underlying information needs from heterogeneous signals.<n>We present a reinforcement learning framework that trains large language models to generate high-quality lists of interest-driven news search queries.
• Score: 7.070021001906444
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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