As information filtering services, recommender systems have extremely
enriched our daily life by providing personalized suggestions and facilitating
people in decision-making, which makes them vital and indispensable to human
society in the information era. However, as people become more dependent on
them, recent studies show that recommender systems potentially own
unintentional impacts on society and individuals because of their unfairness
(e.g., gender discrimination in job recommendations). To develop trustworthy
services, it is crucial to devise fairness-aware recommender systems that can
mitigate these bias issues. In this survey, we summarise existing methodologies
and practices of fairness in recommender systems. Firstly, we present concepts
of fairness in different recommendation scenarios, comprehensively categorize
current advances, and introduce typical methods to promote fairness in
different stages of recommender systems. Next, after introducing datasets and
evaluation metrics applied to assess the fairness of recommender systems, we
will delve into the significant influence that fairness-aware recommender
systems exert on real-world industrial applications. Subsequently, we highlight
the connection between fairness and other principles of trustworthy recommender
systems, aiming to consider trustworthiness principles holistically while
advocating for fairness. Finally, we summarize this review, spotlighting
promising opportunities in comprehending concepts, frameworks, the balance
between accuracy and fairness, and the ties with trustworthiness, with the
ultimate goal of fostering the development of fairness-aware recommender
systems.Comment: 27 pages, 9 figure