Balanced Neighborhoods for Multi-sided Fairness in Recommendation

Robin Burke, Nasim Sonboli, Aldo Ordonez-Gauger
; Proceedings of the 1st Conference on Fairness, Accountability and Transparency, PMLR 81:202-214, 2018.

Abstract

Fairness has emerged as an important category of analysis for machine learning systems in some application areas. In extending the concept of fairness to recommender systems, there is an essential tension between the goals of fairness and those of personalization. However, there are contexts in which equity across recommendation outcomes is a desirable goal. It is also the case that in some applications fairness may be a multisided concept, in which the impacts on multiple groups of individuals must be considered. In this paper, we examine two different cases of fairness-aware recommender systems: consumer-centered and provider-centered. We explore the concept of a balanced neighborhood as a mechanism to preserve personalization in recommendation while enhancing the fairness of recommendation outcomes. We show that a modified version of the Sparse Linear Method (SLIM) can be used to improve the balance of user and item neighborhoods, with the result of achieving greater outcome fairness in real-world datasets with minimal loss in ranking performance.

Cite this Paper


BibTeX
@InProceedings{pmlr-v81-burke18a, title = {Balanced Neighborhoods for Multi-sided Fairness in Recommendation}, author = {Robin Burke and Nasim Sonboli and Aldo Ordonez-Gauger}, booktitle = {Proceedings of the 1st Conference on Fairness, Accountability and Transparency}, pages = {202--214}, year = {2018}, editor = {Sorelle A. Friedler and Christo Wilson}, volume = {81}, series = {Proceedings of Machine Learning Research}, address = {New York, NY, USA}, month = {23--24 Feb}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v81/burke18a/burke18a.pdf}, url = {http://proceedings.mlr.press/v81/burke18a.html}, abstract = {Fairness has emerged as an important category of analysis for machine learning systems in some application areas. In extending the concept of fairness to recommender systems, there is an essential tension between the goals of fairness and those of personalization. However, there are contexts in which equity across recommendation outcomes is a desirable goal. It is also the case that in some applications fairness may be a multisided concept, in which the impacts on multiple groups of individuals must be considered. In this paper, we examine two different cases of fairness-aware recommender systems: consumer-centered and provider-centered. We explore the concept of a balanced neighborhood as a mechanism to preserve personalization in recommendation while enhancing the fairness of recommendation outcomes. We show that a modified version of the Sparse Linear Method (SLIM) can be used to improve the balance of user and item neighborhoods, with the result of achieving greater outcome fairness in real-world datasets with minimal loss in ranking performance.} }
Endnote
%0 Conference Paper %T Balanced Neighborhoods for Multi-sided Fairness in Recommendation %A Robin Burke %A Nasim Sonboli %A Aldo Ordonez-Gauger %B Proceedings of the 1st Conference on Fairness, Accountability and Transparency %C Proceedings of Machine Learning Research %D 2018 %E Sorelle A. Friedler %E Christo Wilson %F pmlr-v81-burke18a %I PMLR %J Proceedings of Machine Learning Research %P 202--214 %U http://proceedings.mlr.press %V 81 %W PMLR %X Fairness has emerged as an important category of analysis for machine learning systems in some application areas. In extending the concept of fairness to recommender systems, there is an essential tension between the goals of fairness and those of personalization. However, there are contexts in which equity across recommendation outcomes is a desirable goal. It is also the case that in some applications fairness may be a multisided concept, in which the impacts on multiple groups of individuals must be considered. In this paper, we examine two different cases of fairness-aware recommender systems: consumer-centered and provider-centered. We explore the concept of a balanced neighborhood as a mechanism to preserve personalization in recommendation while enhancing the fairness of recommendation outcomes. We show that a modified version of the Sparse Linear Method (SLIM) can be used to improve the balance of user and item neighborhoods, with the result of achieving greater outcome fairness in real-world datasets with minimal loss in ranking performance.
APA
Burke, R., Sonboli, N. & Ordonez-Gauger, A.. (2018). Balanced Neighborhoods for Multi-sided Fairness in Recommendation. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, in PMLR 81:202-214

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