With the widespread use of Internet of things (IoT), mobile phones, connected devices and
artificial intelligence (AI), recommender systems (RSs) have become a booming technology …

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 …

This article introduces algorithmic bias in machine learning (ML) based marketing models.
Although the dramatic growth of algorithmic decision making continues to gain momentum in …

The purpose of this study is to present an exhaustive analysis on research paper
recommender systems which have become very popular and gained a lot of research …

Traditionally, machine learning algorithms relied on reliable labels from experts to build
predictions. More recently however, algorithms have been receiving data from the general …

As recommender systems play an important role in everyday life, there is an increasing
pressure that such systems are fair. Besides serving diverse groups of users, recommenders …

Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative
filtering (CF) through the modeling of user-item interaction graphs. The nucleus of existing …

Recommender systems learn from historical users' feedback that is often non-uniformly
distributed across items. As a consequence, these systems may end up suggesting popular …

In the absence of user profile information, recommender systems have to only rely on current
session information for recommendation. E-commerce sites may use transitions between …

Duration bias widely exists in video recommendations, where models tend to recommend
short videos for the higher ratio of finish playing and thus possibly fail to capture users' true …