Multisided Fairness for Recommendation. Burke, R. .
Multisided Fairness for Recommendation [link]Paper  abstract   bibtex   
Recent work on machine learning has begun to consider issues of fairness. In this paper, we extend the concept of fairness to recommendation. In particular, we show that in some recommendation contexts, fairness may be a multisided concept, in which fair outcomes for multiple individuals need to be considered. Based on these considerations, we present a taxonomy of classes of fairness-aware recommender systems and suggest possible fairness-aware recommendation architectures.
@unpublished{burke_multisided_2017,
	title = {Multisided Fairness for Recommendation},
	url = {http://arxiv.org/abs/1707.00093},
	abstract = {Recent work on machine learning has begun to consider issues of fairness.
In this paper, we extend the concept of fairness to recommendation. In
particular, we show that in some recommendation contexts, fairness may be
a multisided concept, in which fair outcomes for multiple individuals need
to be considered. Based on these considerations, we present a taxonomy of
classes of fairness-aware recommender systems and suggest possible
fairness-aware recommendation architectures.},
	author = {Burke, Robin},
	date = {2017-07-01},
	keywords = {Fall 2017 {IR} Fairness}
}
Downloads: 0