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

Reinforcement Learning (RL)-Based Recommender Systems (RSs) are increasingly
recognized for their ability to improve long-term user engagement. Yet, the field grapples …

Reinforcement Learning (RL)-Based Recommender Systems (RSs) have gained rising
attention for their potential to enhance long-term user engagement. However, research in …

We develop and evaluate neural architectures to model the user behavior in recommender
systems (RS) inspired by click models for Web search but going beyond standard click …

Recommender systems are expected to be assistants that help human users find relevant
information automatically without explicit queries. As recommender systems evolve …

Recommendation systems are crucial in navigating the vast digital market. However, user
data's dynamic and non-stationary nature often hinders their efficacy. Traditional models …

Reinforcement learning (RL) has been widely applied in recommendation systems due to its
potential in optimizing the long-term engagement of users. From the perspective of RL …

Reinforcement learning (RL) has gained popularity in the realm of recommender systems
due to its ability to optimize long-term rewards and guide users in discovering relevant …

Recommender systems have become vital in managing information overload and boosting
user engagement. However, traditional methods rely on fixed user preferences inferred from …

Reinforcement learning (RL) has gained popularity in the realm of recommender systems
due to its ability to optimize long-term rewards and guide users in discovering relevant …