Recommender systems have been widely applied in different real-life scenarios to help us
find useful information. In particular, reinforcement learning (RL)-based recommender …

In this paper, we argue that the paradigm commonly adopted for offline evaluation of
sequential recommender systems is unsuitable for evaluating reinforcement learning-based …

Simulators can provide valuable insights for researchers and practitioners who wish to
improve recommender systems, because they allow one to easily tweak the experimental …

In recent years, Reinforcement Learning (RL) has shown great promise in session-based
recommendation. Sequential models that use RL have reached state-of-the-art performance …

With the advent of the Internet, individuals have access to an ever-increasing array of
external information sources, and as such, advertising information has undergone a …

Abstract In recent years, Reinforcement Learning (RL) has shown great promise in
sessionbased recommendation. Sequential models that use RL have reached state-of-the …