A common approach for consumer and provider fairness in recommendations

We present a common approach for handling consumer and provider fairness in recommendations. Our solution requires defining two key components, a classification of items and a target distribution, which together define the case of perfect fairness. This formulation allows distinct fairness concepts...

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المؤلفون الرئيسيون:	Sacharidis, Dimitris, MOURATIDIS, Kyriakos, Kleftogiannis, Dimitrios
التنسيق:	text
اللغة:	English
منشور في:	Institutional Knowledge at Singapore Management University 2019
الموضوعات:	Fairness Recommender systems E-Commerce Theory and Algorithms
الوصول للمادة أونلاين:	https://ink.library.smu.edu.sg/sis_research/6943 https://ink.library.smu.edu.sg/context/sis_research/article/7946/viewcontent/CommonApproachConsumerFairness_2019.pdf
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المؤسسة:	Singapore Management University
اللغة:	English

id	sg-smu-ink.sis_research-7946
record_format	dspace
spelling	sg-smu-ink.sis_research-79462022-03-04T09:10:10Z A common approach for consumer and provider fairness in recommendations Sacharidis, Dimitris MOURATIDIS, Kyriakos Kleftogiannis, Dimitrios We present a common approach for handling consumer and provider fairness in recommendations. Our solution requires defining two key components, a classification of items and a target distribution, which together define the case of perfect fairness. This formulation allows distinct fairness concepts to be specified in a common framework. We further propose a novel reranking algorithm that optimizes for a desired trade-off between utility and fairness of a recommendation list. 2019-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6943 info:doi/10.1145/3298689.3346970 https://ink.library.smu.edu.sg/context/sis_research/article/7946/viewcontent/CommonApproachConsumerFairness_2019.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Fairness Recommender systems E-Commerce Theory and Algorithms
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	Fairness Recommender systems E-Commerce Theory and Algorithms
spellingShingle	Fairness Recommender systems E-Commerce Theory and Algorithms Sacharidis, Dimitris MOURATIDIS, Kyriakos Kleftogiannis, Dimitrios A common approach for consumer and provider fairness in recommendations
description	We present a common approach for handling consumer and provider fairness in recommendations. Our solution requires defining two key components, a classification of items and a target distribution, which together define the case of perfect fairness. This formulation allows distinct fairness concepts to be specified in a common framework. We further propose a novel reranking algorithm that optimizes for a desired trade-off between utility and fairness of a recommendation list.
format	text
author	Sacharidis, Dimitris MOURATIDIS, Kyriakos Kleftogiannis, Dimitrios
author_facet	Sacharidis, Dimitris MOURATIDIS, Kyriakos Kleftogiannis, Dimitrios
author_sort	Sacharidis, Dimitris
title	A common approach for consumer and provider fairness in recommendations
title_short	A common approach for consumer and provider fairness in recommendations
title_full	A common approach for consumer and provider fairness in recommendations
title_fullStr	A common approach for consumer and provider fairness in recommendations
title_full_unstemmed	A common approach for consumer and provider fairness in recommendations
title_sort	common approach for consumer and provider fairness in recommendations
publisher	Institutional Knowledge at Singapore Management University
publishDate	2019
url	https://ink.library.smu.edu.sg/sis_research/6943 https://ink.library.smu.edu.sg/context/sis_research/article/7946/viewcontent/CommonApproachConsumerFairness_2019.pdf
_version_	1770576163727474688

A common approach for consumer and provider fairness in recommendations

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