Optimized Recommender Systems with Deep Reinforcement Learning
This work addresses the need for better recommender systems in online retail, but it appears incremental as it focuses on evaluating existing methods rather than introducing new ones.
The paper tackles the problem of improving recommender systems by evaluating state-of-the-art reinforcement learning algorithms in a realistic testbed, but it does not report specific numerical results or gains.
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.