A group recommender system (GRS) generates suggestions for a group of individuals,
considering not only each person's preferences but also factors such as social dynamics …

A typical benchmark dataset for recommender system (RecSys) evaluation consists of user-
item interactions generated on a platform within a time period. The interaction generation …

This survey explores the fairness of large language models (LLMs) in e-commerce,
examining their progress, applications, and the challenges they face. LLMs have become …

When implicit feedback recommender systems expose users to items, they influence the
users' choices and, consequently, their own future recommendations. This effect is known as …

The explosive growth of the API economy in recent years has led to a dramatic increase in
available APIs. Mashup development, a dominant approach for creating data-centric …

Many modern online services feature personalized recommendations. A central challenge
when providing such recommendations is that the reason why an individual user accesses …

Recommender Systems (RecSys) have become indispensable in numerous applications,
profoundly influencing our everyday experiences. Despite their practical significance …

Recommendation systems are widespread, and through customized recommendations,
promise to match users with options they will like. To that end, data on engagement is …

Recommender systems have become a pervasive part of our daily online experience, and
are one of the most widely used applications of artificial intelligence and machine learning …

Many recent studies have investigated social biases in LLMs but brand bias has received
little attention. This research examines the biases exhibited by LLMs towards different …