research-article

Exploring author gender in book rating and recommendation

Authors:

Michael D. Ekstrand,

Mohammed R. Imran Kazi,

Hoda Mehrpouyan,

Daniel KluverAuthors Info & Claims

RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

Pages 242 - 250

https://doi.org/10.1145/3240323.3240373

Published: 27 September 2018 Publication History

Abstract

Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items. Many of the patterns in rating datasets reflect important real-world differences between the various users and items in the data; other patterns may be irrelevant or possibly undesirable for social or ethical reasons, particularly if they reflect undesired discrimination, such as gender or ethnic discrimination in publishing. In this work, we examine the response of collaborative filtering recommender algorithms to the distribution of their input data with respect to a dimension of social concern, namely content creator gender. Using publicly-available book ratings data, we measure the distribution of the genders of the authors of books in user rating profiles and recommendation lists produced from this data. We find that common collaborative filtering algorithms differ in the gender distribution of their recommendation lists, and in the relationship of that output distribution to user profile distribution.

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Cited By

Shao PWu LZhang KLian DHong RLi YWang M(2024)Average User-Side Counterfactual Fairness for Collaborative FilteringACM Transactions on Information Systems10.1145/365663942:5(1-26)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3656639
Ahn YLin Y(2024)Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender SystemsACM Transactions on Intelligent Systems and Technology10.1145/365004415:4(1-20)Online publication date: 29-Feb-2024
https://dl.acm.org/doi/10.1145/3650044
Ferraro AFerreira GDiaz FBorn G(2024)Measuring Commonality in Recommendation of Cultural Content to Strengthen Cultural CitizenshipACM Transactions on Recommender Systems10.1145/36431382:1(1-32)Online publication date: 1-Feb-2024
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Index Terms

Exploring author gender in book rating and recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
2. Social and professional topics
  1. User characteristics
    1. Gender

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cover image ACM Conferences

RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

September 2018

600 pages

ISBN:9781450359016

DOI:10.1145/3240323

General Chairs:
Sole Pera
Boise State University
,
Michael Ekstrand
Boise State University
,
Program Chairs:
Xavier Amatriain
Curai, USA
,
John O'Donovan
University of California, Santa Barbara

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

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Published: 27 September 2018

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RecSys '18

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RecSys '18: Twelfth ACM Conference on Recommender Systems

October 2, 2018

British Columbia, Vancouver, Canada

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RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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RecSys '24

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sigchi

18th ACM Conference on Recommender Systems

October 14 - 18, 2024

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Cited By

Shao PWu LZhang KLian DHong RLi YWang M(2024)Average User-Side Counterfactual Fairness for Collaborative FilteringACM Transactions on Information Systems10.1145/365663942:5(1-26)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3656639
Ahn YLin Y(2024)Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender SystemsACM Transactions on Intelligent Systems and Technology10.1145/365004415:4(1-20)Online publication date: 29-Feb-2024
https://dl.acm.org/doi/10.1145/3650044
Ferraro AFerreira GDiaz FBorn G(2024)Measuring Commonality in Recommendation of Cultural Content to Strengthen Cultural CitizenshipACM Transactions on Recommender Systems10.1145/36431382:1(1-32)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1145/3643138
Zhang DZhang KWu LTian MHong RWang MBaeza-Yates RBonchi F(2024)Path-Specific Causal Reasoning for Fairness-aware Cognitive DiagnosisProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672049(4143-4154)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672049
Kruse JMichiels LStarke ATintarev NVrijenhoek S(2024)NORMalize: A Tutorial on the Normative Design and Evaluation of Information Access SystemsProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638319(422-424)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638319
Gómez EContreras DSalamo MBoratto L(2024)Bringing Equity to Coarse and Fine-Grained Provider Groups in Recommender SystemsProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659552(18-23)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659552
Caton SHaas C(2024)Fairness in Machine Learning: A SurveyACM Computing Surveys10.1145/361686556:7(1-38)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3616865
Saxena SJain S(2024)Exploring and mitigating gender bias in book recommender systems with explicit feedbackJournal of Intelligent Information Systems10.1007/s10844-023-00827-8Online publication date: 25-Mar-2024
https://doi.org/10.1007/s10844-023-00827-8
Wang XLi HOuyang WLi YBian YHu M(2024)Personalized Vehicle Task Offloading Based on the Recommender SystemDevelopments and Applications in SmartRail, Traffic, and Transportation Engineering10.1007/978-981-97-3682-9_39(420-432)Online publication date: 14-Aug-2024
https://doi.org/10.1007/978-981-97-3682-9_39
Melchiorre AMasoudian SKumar DSchedl M(2024)Modular Debiasing of Latent User Representations in Prototype-Based Recommender SystemsMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70341-6_4(56-72)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70341-6_4
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