Optimized Recommender Systems with Deep Reinforcement Learning

TL;DR

This paper explores the application of deep reinforcement learning to optimize recommender systems, providing a reproducible testbed for evaluating various algorithms in realistic settings.

Contribution

It introduces a reproducible testbed for deep reinforcement learning in recommender systems and evaluates state-of-the-art algorithms within this environment.

Findings

Reinforcement learning can effectively improve recommendation quality.

A standardized testbed facilitates fair comparison of algorithms.

Deep RL algorithms outperform traditional methods in certain metrics.

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.