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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 recommender systems: research landscape and future directions
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 …
social media, and thus impact our beliefs, decisions, and actions. At the same time, these …
Fairness in recommendation: Foundations, methods, and applications
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 …
Manipulating recommender systems: A survey of poisoning attacks and countermeasures
Recommender systems have become an integral part of online services due to their ability to
help users locate specific information in a sea of data. However, existing studies show that …
help users locate specific information in a sea of data. However, existing studies show that …
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 trustworthy recommender systems
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 …
deployed in almost every corner of the web and facilitate the human decision-making …
Explainable fairness in recommendation
Existing research on fairness-aware recommendation has mainly focused on the
quantification of fairness and the development of fair recommendation models, neither of …
quantification of fairness and the development of fair recommendation models, neither of …
[HTML][HTML] A unifying and general account of fairness measurement in recommender systems
Fairness is fundamental to all information access systems, including recommender systems.
However, the landscape of fairness definition and measurement is quite scattered with many …
However, the landscape of fairness definition and measurement is quite scattered with many …
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 …
Toward pareto efficient fairness-utility trade-off in recommendation through reinforcement learning
The issue of fairness in recommendation is becoming increasingly essential as
Recommender Systems (RS) touch and influence more and more people in their daily lives …
Recommender Systems (RS) touch and influence more and more people in their daily lives …