Recommender Systems have become a very useful tool for a large variety of domains.
Researchers have been attempting to improve their algorithms in order to issue better …

Ranking, recommendation, and retrieval systems are widely used in online platforms and
other societal systems, including e-commerce, media-streaming, admissions, gig platforms …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Diversification has become one of the leading topics of recommender system research not
only as a way to solve the over-fitting problem but also an approach to increasing the quality …

In the debate about filter bubbles caused by algorithmic news recommendation, the
conceptualization of the two core concepts in this debate, diversity and algorithms, has …

Novelty and diversity have been identified, along with accuracy, as prominent properties of
useful recommendations. Considerable progress has been made in the field in terms of the …

The issue of fairness in recommendation is becoming increasingly essential as
Recommender Systems (RS) touch and influence more and more people in their daily lives …

This chapter aims to provide an overview of the class of multi-criteria recommender systems.
First, it defines the recommendation problem as a multicriteria decision making (MCDM) …

Recommender System (RS) is an important online application that affects billions of users
every day. The mainstream RS ranking framework is composed of two parts: a Multi-Task …

Recommendation with multiple objectives is an important but difficult problem, where the
coherent difficulty lies in the possible conflicts between objectives. In this case, multi …