An increasing number of decisions regarding the daily lives of human beings are being
controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging …

The comprehensive evaluation of the performance of a recommender system is a complex
endeavor: many facets need to be considered in configuring an adequate and effective …

Recommender systems are an essential tool to relieve the information overload challenge
and play an important role in people's daily lives. Since recommendations involve …

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

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 …

When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

As a highly data-driven application, recommender systems could be affected by data bias,
resulting in unfair results for different data groups, which could be a reason that affects the …

In the past few years, there has been much work on incorporating fairness requirements into
algorithmic rankers, with contributions coming from the data management, algorithms …

We present a large-scale study of gender bias in occupation classification, a task where the
use of machine learning may lead to negative outcomes on peoples' lives. We analyze the …

We present a framework for quantifying and mitigating algorithmic bias in mechanisms
designed for ranking individuals, typically used as part of web-scale search and …