Recommender systems can strongly influence which information we see online, eg, on
social media, and thus impact our beliefs, decisions, and actions. At the same time, these …

As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

The remarkable achievements of Large Language Models (LLMs) have led to the
emergence of a novel recommendation paradigm—Recommendation via LLM (RecLLM) …

Research in Fairness, Accountability, Transparency, and Ethics (FATE) 1 has established
many sources and forms of algorithmic harm, in domains as diverse as health care, finance …

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

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

Reinforcement Learning (RL)-based recommender systems (RSs) have garnered
considerable attention due to their ability to learn optimal recommendation policies and …

Fairness is fundamental to all information access systems, including recommender systems.
However, the landscape of fairness definition and measurement is quite scattered with many …

Achieving fairness over different user groups in recommender systems is an important
problem. The majority of existing works achieve fairness through constrained optimization …

Recommender systems (RS) are effective tools for mitigating information overload and have
seen extensive applications across various domains. However, the single focus on utility …