FedSlate:A Federated Deep Reinforcement Learning Recommender System
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Abstract (arXiv)
Reinforcement learning methods have been used to optimize long-term user engagement in recommendation systems. However, existing reinforcement learning-based recommendation systems do not fully exploit the relevance of individual user behavior across different platforms. One potential solution is to aggregate data from various platforms in a centralized location and use the aggregated data for training. However, this approach raises economic and legal concerns, including increased communication costs and potential threats to user privacy. To address these challenges, we propose FedSlate, a federated reinforcement learning recommendation algorithm that effectively utilizes information that is prohibited from being shared at a legal level. We employ the SlateQ algorithm to assist FedSlate in learning users' long-term behavior and evaluating the value of recommended content. We extend the existing application scope of recommendation systems from single-user single-platform to single-user multi-platform and address cross-platform learning challenges by introducing federated learning. We use RecSim to construct a simulation environment for evaluating FedSlate and compare its performance with state-of-the-art benchmark recommendation models. Experimental results demonstrate the superior effects of FedSlate over baseline methods in various environmental settings, and FedSlate facilitates the learning of recommendation strategies in scenarios where baseline methods are completely inapplicable. Code is available at textithttps://github.com/TianYaDY/FedSlate.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 20 of its 59 references that are in the map. First posted on arXiv 23 Sep 2024.
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20 of 59 references.
References · 20
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- 2019DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systemscited by 44
- 2019Federated Deep Reinforcement Learningcited by 34
- 2021A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directionscited by 19
- 2022Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoTcited by 15
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- 2023A Privacy Preserving System for Movie Recommendations Using Federated Learningcited by 6
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