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Multisided Fairness for Recommendation.
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TLDR
It is shown that in some recommendation contexts, fairness may be a multisided concept, in which fair outcomes for multiple individuals need to be considered, and a taxonomy of classes of fairness-aware recommender systems is presented.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.read more
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Dissertation
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References
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Proceedings ArticleDOI
Fairness through awareness
TL;DR: A framework for fair classification comprising a (hypothetical) task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand and an algorithm for maximizing utility subject to the fairness constraint, that similar individuals are treated similarly is presented.
Posted Content
Fairness Through Awareness
TL;DR: In this article, the authors proposed a framework for fair classification comprising a task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand, and an algorithm for maximizing utility subject to the fairness constraint that similar individuals are treated similarly.
Proceedings Article
Learning Fair Representations
TL;DR: A learning algorithm for fair classification that achieves both group fairness (the proportion of members in a protected group receiving positive classification is identical to the proportion in the population as a whole), and individual fairness (similar individuals should be treated similarly).
Journal ArticleDOI
Internet Advertising and the Generalized Second-Price Auction: Selling Billions of Dollars Worth of Keywords
TL;DR: In this article, the authors investigate the generalized second-price (GSP) auction, a new mechanism used by search engines to sell online advertising, and show that it has a unique equilibrium, with the same payoffs to all players as the dominant strategy equilibrium of VCG.
Book ChapterDOI
Advances in Collaborative Filtering
Yehuda Koren,Robert M. Bell +1 more
TL;DR: In this paper, the authors survey the recent progress in the field of collaborative filtering and describe several extensions that bring competitive accuracy into neighborhood methods, which used to dominate the field and demonstrate how to utilize temporal models and implicit feedback to extend models accuracy.