# Causally-Informed Reinforcement Learning for Adaptive Emotion-Aware Social Media Recommendation

arXiv: https://arxiv.org/abs/2511.14768 · HTML: https://mlsanity.com/paper/2511.14768/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 53 references that are in the map.
- First posted on arXiv 1 Oct 2025.

## Builds on (most cited first, 16 of 16)

- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [Causal Effect Inference with Deep Latent-Variable Models](https://mlsanity.com/paper/1705.08821/) (2017)
- [Reinforcement learning](https://mlsanity.com/paper/2405.10369/) (2024)
- [Explainable Reinforcement Learning Through a Causal Lens](https://mlsanity.com/paper/1905.10958/) (2019)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- ['It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions](https://mlsanity.com/paper/1801.10408/) (2018)
- [Causal Embeddings for Recommendation](https://mlsanity.com/paper/1706.07639/) (2017)
- [Causal Inference in Recommender Systems: A Survey and Future Directions](https://mlsanity.com/paper/2208.12397/) (2022)
- [Deep Reinforcement Learning for List-wise Recommendations](https://mlsanity.com/paper/1801.00209/) (2017)
- [Conversational Contextual Bandit: Algorithm and Application](https://mlsanity.com/paper/1906.01219/) (2019)
- [Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations](https://mlsanity.com/paper/2301.04945/) (2023)
- [RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender System](https://mlsanity.com/paper/2110.11073/) (2021)
- [Causal Embeddings for Recommendation: An Extended Abstract](https://mlsanity.com/paper/1904.05165/) (2019)

## Built on by (most cited first, 1 of 1)

- [Affective Music Recommendation: A Rollout-Based World Model for Offline Preference Optimization](https://mlsanity.com/paper/2605.28810/) (2026)

## Abstract (arXiv)

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.