基于情绪变化动态调整推荐，兼顾用户情感健康与点击率。
Causally-Informed Reinforcement Learning for Adaptive Emotion-Aware Social Media Recommendation
- 用Transformer预测情绪，结合轻量模型与强化学习动态推荐
- 30天实测显示情绪恢复更快、波动更小，点击率未下降
- 适合关注用户长期体验的社交平台推荐系统
社交媒体推荐系统在塑造用户情绪体验中起关键作用。但多数系统仅优化点击率、观看时长等参与度指标，忽视用户情绪状态。反复接触情绪化内容会随时间损害用户情绪健康。本文提出情绪感知推荐框架ESMR，根据用户情绪演变轨迹个性化推荐。ESMR融合基于Transformer的情绪预测器与混合推荐策略：稳定期使用LightGBM模型提升参与度，情绪持续低落时启用因果激励的强化学习代理。基于30天交互轨迹的行为验证表明，该框架显著改善情绪恢复能力、降低波动性，且保持高参与度留存。该方法为兼顾情感关怀与推荐性能提供了可行路径。
原文摘要 · Abstract (English)
Social media recommendation systems play a central role in shaping users' emotional experiences. However, most systems are optimized solely for engagement metrics, such as click rate, viewing time, or scrolling, without accounting for users' emotional states. Repeated exposure to emotionally charged content has been shown to negatively affect users' emotional well-being over time. We propose an Emotion-aware Social Media Recommendation (ESMR) framework that personalizes content based on users' evolving emotional trajectories. ESMR integrates a Transformer-based emotion predictor with a hybrid recommendation policy: a LightGBM model for engagement during stable periods and a reinforcement learning agent with causally informed rewards when negative emotional states persist. Through behaviorally grounded evaluation over 30-day interaction traces, ESMR demonstrates improved emotional recovery, reduced volatility, and strong engagement retention. ESMR offers a path toward emotionally aware recommendations without compromising engagement performance.
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