Recommender systems help people find relevant content in a personalized way. One main
promise of such systems is that they are able to increase the visibility of items in the long tail …

Recently, there has been a rising awareness that when machine learning (ML) algorithms
are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or …

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

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 …

Recent work in recommender systems mainly focuses on fairness in recommendations as
an important aspect of measuring recommendations quality. A fairness-aware recommender …

Recommender Systems (RSs) are used to provide users with personalized item
recommendations and help them overcome the problem of information overload. Currently …

Conversational Recommendation System (CRS) is a rapidly growing research area that has
gained significant attention alongside advancements in language modelling techniques …

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

Recommending appropriate travel destinations to consumers based on contextual
information such as their check-in time and location is a primary objective of Point-of-Interest …

Recommender systems are subject to well‐known popularity bias issues, that is, they
expose frequently rated items more in recommendation lists than less‐rated ones. Such a …