Fairness in rankings and recommendations: an overview
We increasingly depend on a variety of data-driven algorithmic systems to assist us in many
aspects of life. Search engines and recommender systems among others are used as …
aspects of life. Search engines and recommender systems among others are used as …
Fairness in ranking, part ii: Learning-to-rank and recommender systems
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 …
algorithmic rankers, with contributions coming from the data management, algorithms …
Elliot: A comprehensive and rigorous framework for reproducible recommender systems evaluation
Recommender Systems have shown to be an effective way to alleviate the over-choice
problem and provide accurate and tailored recommendations. However, the impressive …
problem and provide accurate and tailored recommendations. However, the impressive …
Fairness-aware news recommendation with decomposed adversarial learning
News recommendation is important for online news services. Existing news
recommendation models are usually learned from users' news click behaviors. Usually the …
recommendation models are usually learned from users' news click behaviors. Usually the …
Up5: Unbiased foundation model for fairness-aware recommendation
Recent advancements in foundation models such as large language models (LLM) have
propelled them to the forefront of recommender systems (RS). Moreover, fairness in RS is …
propelled them to the forefront of recommender systems (RS). Moreover, fairness in RS is …
Fair ranking: a critical review, challenges, and future directions
Ranking, recommendation, and retrieval systems are widely used in online platforms and
other societal systems, including e-commerce, media-streaming, admissions, gig platforms …
other societal systems, including e-commerce, media-streaming, admissions, gig platforms …
Fairness in ranking: A survey
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 …
algorithmic rankers, with contributions coming from the data management, algorithms …
Societal biases in retrieved contents: Measurement framework and adversarial mitigation of bert rankers
Societal biases resonate in the retrieved contents of information retrieval (IR) systems,
resulting in reinforcing existing stereotypes. Approaching this issue requires established …
resulting in reinforcing existing stereotypes. Approaching this issue requires established …
Tutorial on fairness of machine learning in recommender systems
Recently, there has been growing attention on fairness considerations in machine learning.
As one of the most pervasive applications of machine learning, recommender systems are …
As one of the most pervasive applications of machine learning, recommender systems are …
Matching algorithms: Fundamentals, applications and challenges
Matching plays a vital role in the rational allocation of resources in many areas, ranging from
market operation to people's daily lives. In economics, the term matching theory is coined for …
market operation to people's daily lives. In economics, the term matching theory is coined for …