Optimized Recommender Systems with Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2110.03039v1
• Date: Wed, 6 Oct 2021 19:54:55 GMT
• Title: Optimized Recommender Systems with Deep Reinforcement Learning
• Authors: Lucas Farris
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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