The comprehensive evaluation of the performance of a recommender system is a complex
endeavor: many facets need to be considered in configuring an adequate and effective …

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 …

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 …

A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature
relevance techniques to explain a deep neural network (DNN) output or explaining models …

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

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

To alleviate sparsity and cold start problem of collaborative filtering based recommender
systems, researchers and engineers usually collect attributes of users and items, and design …

Recent advances in personalized recommendation have sparked great interest in the
exploitation of rich structured information provided by knowledge graphs. Unlike most …

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 …