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 …

While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …

In offline reinforcement learning (RL), the goal is to learn a highly rewarding policy based
solely on a dataset of historical interactions with the environment. This serves as an extreme …

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 …

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 …

We introduce the weighted likelihood bootstrap (WLB) as a way to simulate approximately
from a posterior distribution. This method is often easy to implement, requiring only an …

Deep reinforcement learning (DRL) integrates the feature representation ability of deep
learning with the decision-making ability of reinforcement learning so that it can achieve …

As Recommender Systems (RS) influence more and more people in their daily life, the issue
of fairness in recommendation is becoming more and more important. Most of the prior …

Recommender systems are one of the most pervasive applications of machine learning in
industry, with many services using them to match users to products or information. As such it …