Summary of Cache-aware Reinforcement Learning in Large-scale Recommender Systems, by Xiaoshuang Chen et al.
Cache-Aware Reinforcement Learning in Large-Scale Recommender Systems
by Xiaoshuang Chen, Gengrui Zhang, Yao Wang, Yulin Wu, Shuo Su, Kaiqiao Zhan, Ben Wang
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper proposes a novel approach to optimize large-scale recommender systems by jointly utilizing real-time computation and caching. The authors develop a cache-aware reinforcement learning (CARL) method that formulates the recommendation problem as a Markov decision process with user states and a cache state. This model takes into account the computational load of the system, which determines the cache state. By applying reinforcement learning to this model, the authors aim to improve user engagement over multiple requests. Moreover, they introduce an eigenfunction learning (EL) method to address the critic dependency challenge introduced by caching. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem faced by large-scale recommender systems that struggle with real-time computation during peak periods. It proposes a cache-aware approach that jointly optimizes recommendation using both real-time computation and caching. The authors develop a Markov decision process model to formulate this problem and apply reinforcement learning to improve user engagement. |
Keywords
» Artificial intelligence » Reinforcement learning
