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Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems. (arXiv:2002.07786v1 [cs.IR])

(Submitted on 18 Feb 2020)

Abstract: The proliferation of personalized recommendation technologies has raised
concerns about discrepancies in their recommendation performance across
different genders, age groups, and racial or ethnic populations. This varying
degree of performance could impact users’ trust in the system and may pose
legal and ethical issues in domains where fairness and equity are critical
concerns, like job recommendation. In this paper, we investigate several
potential factors that could be associated with discriminatory performance of a
recommendation algorithm for women versus men. We specifically study several
characteristics of user profiles and analyze their possible associations with
disparate behavior of the system towards different genders. These
characteristics include the anomaly in rating behavior, the entropy of users’
profiles, and the users’ profile size. Our experimental results on a public
dataset using four recommendation algorithms show that, based on all the three
mentioned factors, women get less accurate recommendations than men indicating
an unfair nature of recommendation algorithms across genders.

Submission history

From: Masoud Mansoury [view email]

Tue, 18 Feb 2020 18:30:17 UTC (5,266 KB)

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