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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
Citations
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Journal ArticleDOI
We’re in This Together: A Multi-Stakeholder Approach for News Recommenders
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Towards user-oriented privacy for recommender system data : A personalization-based approach to gender obfuscation for user profiles
TL;DR: Personalized Blurring (PerBlur) as mentioned in this paper is a privacy solution for the data used to train a recommender system, i.e., the user-item matrix.
Book ChapterDOI
Facets of Fairness in Search and Recommendation
TL;DR: Comparisons and highlights contracts among various measures, and gaps in conceptual and evaluative frameworks are presented in the emerging concept of fairness in various recommendation settings.
Proceedings ArticleDOI
Optimizing Generalized Gini Indices for Fairness in Rankings
Virginie Do,Nicolas Usunier +1 more
TL;DR: This paper explores the use of generalized Gini welfare functions (GGFs) as a means to specify the normative criterion that recommender systems should optimize for, and presents experiments using real datasets with up to 15k users and items to show that this approach obtains better trade-offs than the baselines on a variety of recommendation tasks and fairness criteria.
Journal ArticleDOI
Fairness in Music Recommender Systems: A Stakeholder-Centered Mini Review
TL;DR: This narrative literature review shows that the large majority of works analyze the current situation of fairness in music recommender systems, whereas only a few works propose approaches to improve it, which is a promising direction for future research.
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.