Summary of Rpaf: a Reinforcement Prediction-allocation Framework For Cache Allocation in Large-scale Recommender Systems, by Shuo Su et al.
RPAF: A Reinforcement Prediction-Allocation Framework for Cache Allocation in Large-Scale Recommender Systems
by Shuo Su, Xiaoshuang Chen, Yao Wang, Yulin Wu, Ziqiang Zhang, Kaiqiao Zhan, Ben Wang, Kun Gai
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper proposes a reinforcement prediction-allocation framework (RPAF) for recommender systems to maximize user engagement while satisfying computational budget constraints. The authors identify two key challenges: the value-strategy dependency and streaming allocation, which they address through RPAF’s prediction and allocation stages. The framework uses reinforcement learning to estimate cache values considering the value-strategy dependency and determine cache choices for each request while adhering to global budget constraints. To train RPAF, a relaxed local allocator (RLA) is introduced to handle the globality challenge, and PoolRank is employed in the allocation stage to address streaming allocation issues. Experimental results show that RPAF significantly improves user engagement under computational budget constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create better recommendation systems that can make users happy while also being efficient with computer resources. The authors found two big problems: how to choose what to recommend based on the value it provides, and how to handle sudden changes in what people want. They created a new way called RPAF that uses machine learning to decide what to recommend and when. This framework makes sure to follow rules about how much computer power is allowed, while also making good recommendations. The results show that this new approach works really well and makes users more engaged. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning
