Recommender systems (RSs) have become an inseparable part of our everyday lives. They
help us find our favorite items to purchase, our friends on social networks, and our favorite …

Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …

In light of the emergence of deep reinforcement learning (DRL) in recommender systems
research and several fruitful results in recent years, this survey aims to provide a timely and …

Recommender systems are software applications that help users to find items of interest in
situations of information overload. Current research often assumes a one-shot interaction …

Traditional recommendation systems estimate user preference on items from past interaction
history, thus suffering from the limitations of obtaining fine-grained and dynamic user …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …

The progress of recommender systems is hampered mainly by evaluation as it requires real-
time interactions between humans and systems, which is too laborious and expensive. This …

Reinforcement learning (RL) allows an agent to solve sequential decision-making problems
by interacting with an environment in a trial-and-error fashion. When these environments are …

Conversational recommender systems (CRS) enable the traditional recommender systems
to explicitly acquire user preferences towards items and attributes through interactive …

While personalization increases the utility of recommender systems, it also brings the issue
of filter bubbles. eg, if the system keeps exposing and recommending the items that the user …