Optimized Recommender Systems with Deep Reinforcement Learning
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Abstract (arXiv)
Recommender Systems have been the cornerstone of online retailers. Traditionally they were based on rules, relevance scores, ranking algorithms, and supervised learning algorithms, but now it is feasible to use reinforcement learning algorithms to generate meaningful recommendations. This work investigates and develops means to setup a reproducible testbed, and evaluate different state of the art algorithms in a realistic environment. It entails a proposal, literature review, methodology, results, and comments.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 41 references that are in the map. First posted on arXiv 6 Oct 2021.
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References · 9
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
- 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
- 2016OpenAI Gymcited by 1,736
- 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
- 2017Deep Learning based Recommender System: A Survey and New Perspectivescited by 136
- 2018Deep Reinforcement Learning for Page-wise Recommendationscited by 100
- 2018Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learningcited by 71
- 2020MARS-Gym: A Gym framework to model, train, and evaluate Recommender Systems for Marketplacescited by 13
- 2018Reinforcement Learning based Recommender System using Biclustering Techniquecited by 12
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