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 …

As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

Efficiently navigating a superpressure balloon in the stratosphere requires the integration of
a multitude of cues, such as wind speed and solar elevation, and the process is complicated …

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 provide personalized service support to users by learning their
previous behaviors and predicting their current preferences for particular products. Artificial …

In session-based or sequential recommendation, it is important to consider a number of
factors like long-term user engagement, multiple types of user-item interactions such as …

Planning for both immediate and long-term benefits becomes increasingly important in
recommendation. Existing methods apply Reinforcement Learning (RL) to learn planning …

Over the years we have seen recommender systems shifting focus from optimizing short-
term engagement toward improving long-term user experience on the platforms. While …

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 …

Recommender systems are the algorithms which select, filter, and personalize content
across many of the world's largest platforms and apps. As such, their positive and negative …