Summary of On Causally Disentangled State Representation Learning For Reinforcement Learning Based Recommender Systems, by Siyu Wang and Xiaocong Chen and Lina Yao
On Causally Disentangled State Representation Learning for Reinforcement Learning based Recommender Systems
by Siyu Wang, Xiaocong Chen, Lina Yao
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Reinforcement Learning-based Recommender Systems (RLRS) tackle the complexity and dynamism of user interactions by introducing an innovative causal approach for decomposing the state. This approach, called Causal-Indispensable State Representations (CIDS), identifies Directly Action-Influenced State Variables (DAIS) and Action-Influence Ancestors (AIA). Leveraging conditional mutual information, the framework discerns causal relationships within the generative process and isolates critical state variables from typically dense and high-dimensional state representations. Theoretical evidence supports identifiability of these variables, enabling policy training over a more advantageous subset of the agent’s state space. This approach outperforms state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a new way to recommend things people might like based on how they behave online. They try to understand which parts of the data are most important for making good recommendations and focus on those. This helps the system adapt to changing user preferences and behaviors while still being able to make smart decisions. The team shows that their approach works better than existing methods. |
Keywords
» Artificial intelligence » Reinforcement learning
