用强化学习建模用户兴趣演变，提升多场景推荐效果
Reinforcing User Interest Evolution in Multi-Scenario Learning for recommender systems
- 通过双Q-learning追踪用户兴趣在不同场景中的演化过程
- 对比学习损失结合Q值优化，显著提升下一物品预测准确率
- 适合研究多场景推荐与用户行为建模的学者参考
在真实推荐系统中，用户会经历主页、搜索页、相关推荐页等多种场景，不同场景下关注点各异。然而，由于决策机制和偏好表达方式差异，用户兴趣在不同场景中可能不一致，导致统一建模困难，多场景学习成为挑战。为此，本文提出一种新颖的强化学习方法，通过建模用户兴趣在多场景中的演化过程来捕捉用户偏好。该方法采用双Q-learning提升下一物品预测准确性，并利用Q值优化对比学习损失，进一步增强模型性能。实验表明，该方法在多场景推荐任务中优于现有最先进方法。本工作为多场景建模提供了新视角，指明了未来研究的潜在方向。
原文摘要 · Abstract (English)
In real-world recommendation systems, users would engage in variety scenarios, such as homepages, search pages, and related recommendation pages. Each of these scenarios would reflect different aspects users focus on. However, the user interests may be inconsistent in different scenarios, due to differences in decision-making processes and preference expression. This variability complicates unified modeling, making multi-scenario learning a significant challenge. To address this, we propose a novel reinforcement learning approach that models user preferences across scenarios by modeling user interest evolution across multiple scenarios. Our method employs Double Q-learning to enhance next-item prediction accuracy and optimizes contrastive learning loss using Q-value to make model performance better. Experimental results demonstrate that our approach surpasses state-of-the-art methods in multi-scenario recommendation tasks. Our work offers a fresh perspective on multi-scenario modeling and highlights promising directions for future research.
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