A survey of graph neural networks for recommender systems: Challenges, methods, and directions
Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …
Recently, graph neural networks have become the new state-of-the-art approach to …
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 …
A survey on the fairness of recommender systems
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 …
and play an important role in people's daily lives. Since recommendations involve …
User-oriented fairness in recommendation
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 …
resulting in unfair results for different data groups, which could be a reason that affects the …
Fairness in graph mining: A survey
Graph mining algorithms have been playing a significant role in myriad fields over the years.
However, despite their promising performance on various graph analytical tasks, most of …
However, despite their promising performance on various graph analytical tasks, most of …
Trustworthy recommender systems
Recommender systems (RSs) aim at hel** users to effectively retrieve items of their
interests from a large catalogue. For a quite long time, researchers and practitioners have …
interests from a large catalogue. For a quite long time, researchers and practitioners have …
Towards personalized fairness based on causal notion
Recommender systems are gaining increasing and critical impacts on human and society
since a growing number of users use them for information seeking and decision making …
since a growing number of users use them for information seeking and decision making …
Fairness in recommendation: A survey
As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision making. The satisfaction of users and …
playing an important role on assisting human decision making. The satisfaction of users and …
[HTML][HTML] A survey on fairness-aware recommender systems
As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …
life by providing personalized suggestions and facilitating people in decision-making, which …
Learning fair representations for recommendation: A graph-based perspective
As a key application of artificial intelligence, recommender systems are among the most
pervasive computer aided systems to help users find potential items of interests. Recently …
pervasive computer aided systems to help users find potential items of interests. Recently …