# Generative Inverse Deep Reinforcement Learning for Online Recommendation

arXiv: https://arxiv.org/abs/2011.02248 · HTML: https://mlsanity.com/paper/2011.02248/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2020.

## Built on by (most cited first, 12 of 12)

- [A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions](https://mlsanity.com/paper/2109.03540/) (2021)
- [Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning](https://mlsanity.com/paper/2304.07920/) (2023)
- [Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference](https://mlsanity.com/paper/2105.00822/) (2021)
- [Exploring Gradient Explosion in Generative Adversarial Imitation Learning: A Probabilistic Perspective](https://mlsanity.com/paper/2312.11214/) (2023)
- [Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning based Recommendation Systems](https://mlsanity.com/paper/2403.17634/) (2024)
- [Policy-Guided Causal State Representation for Offline Reinforcement Learning Recommendation](https://mlsanity.com/paper/2502.02327/) (2025)
- [IBCB: Efficient Inverse Batched Contextual Bandit for Behavioral Evolution History](https://mlsanity.com/paper/2403.16075/) (2024)
- [On Causally Disentangled State Representation Learning for Reinforcement Learning based Recommender Systems](https://mlsanity.com/paper/2407.13091/) (2024)
- [MTRec: Learning to Align with User Preferences via Mental Reward Models](https://mlsanity.com/paper/2509.22807/) (2025)
- [Energy-Guided Diffusion Sampling for Long-Term User Behavior Prediction in Reinforcement Learning-based Recommendation](https://mlsanity.com/paper/2510.12815/) (2025)
- [A General Neural Causal Model for Interactive Recommendation](https://mlsanity.com/paper/2310.19519/) (2023)
- [Inverse Theory of Mind Modeling for Content Recommendation: From Web Browsing to Dynamic Intelligent Interfaces](https://mlsanity.com/paper/2608.11354/) (2026)

## Abstract (arXiv)

Deep reinforcement learning enables an agent to capture user's interest through interactions with the environment dynamically. It has attracted great interest in the recommendation research. Deep reinforcement learning uses a reward function to learn user's interest and to control the learning process. However, most reward functions are manually designed; they are either unrealistic or imprecise to reflect the high variety, dimensionality, and non-linearity properties of the recommendation problem. That makes it difficult for the agent to learn an optimal policy to generate the most satisfactory recommendations. To address the above issue, we propose a novel generative inverse reinforcement learning approach, namely InvRec, which extracts the reward function from user's behaviors automatically, for online recommendation. We conduct experiments on an online platform, VirtualTB, and compare with several state-of-the-art methods to demonstrate the feasibility and effectiveness of our proposed approach.