As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …

Recently there has been a growing interest in fairness-aware recommender systems
including fairness in providing consistent performance across different users or groups of …

The popularity bias problem is one of the most prominent challenges of recommender
systems, ie, while a few heavily rated items receive much attention in presented …

Recommender systems are often biased toward popular items. In other words, few items are
frequently recommended while the majority of items do not get proportionate attention. That …

In this paper, we propose a new privacy solution for the data used to train a recommender
system, ie, the user–item matrix. The user–item matrix contains implicit information, which …

In a collaborative-filtering recommendation scenario, biases in the data will likely propagate
in the learned recommendations. In this paper we focus on the so-called mainstream bias …

Recommender systems are important and powerful tools for various personalized services.
Traditionally, these systems use data mining and machine learning techniques to make …

Recommender systems are deployed heavily by many online platforms for better user
engagement and providing recommendations. Despite being so popular, several works …

Recent research in the field of recommender systems focuses on the incorporation of social
information into collaborative filtering methods to improve the reliability of recommendations …

With the continuous development of artificial intelligence (AI), algorithmic discrimination and
discriminatory and misleading content (DMC) generated by AI have given rise to many …