research-article

Fairness and Diversity in Social-Based Recommender Systems

Authors:

Dimitris Sacharidis,

Carine Pierrette Mukamakuza,

Hannes WerthnerAuthors Info & Claims

UMAP '20 Adjunct: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization

Pages 83 - 88

https://doi.org/10.1145/3386392.3397603

Published: 13 July 2020 Publication History

Abstract

In social networks, the phenomena of homophily and influence explain the fact that friends tend to be similar. Social-based recommenders exploit this observation by incorporating the social structure in collaborative filtering techniques. In practice, these recommenders tend to make friends appear more similar compared to non-socially aware techniques. Various proposals have demonstrated the benefit of incorporating social connections. But at what cost? In this work, we show that there exist users that are mistreated in social recommenders. Specifically, their individual preferences are suppressed more compared to other users in their social circle. We seek to identify who they are and develop techniques that protect them, without severely affecting the effectiveness of the recommender.

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Index Terms

Fairness and Diversity in Social-Based Recommender Systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

UMAP '20 Adjunct: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization

July 2020

395 pages

ISBN:9781450379502

DOI:10.1145/3386392

Editors:
Tsvi Kuflik
University of Haifa, Israel
,
Ilaria Torre
University of Genoa, Italy
,
Robin Burke
University of Colorado, Boulder, USA
,
Cristina Gena
University of Turin, Italy

Copyright © 2020 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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Published: 13 July 2020

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UMAP '20

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UMAP '20: 28th ACM Conference on User Modeling, Adaptation and Personalization

July 14 - 17, 2020

Genoa, Italy

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Overall Acceptance Rate 162 of 633 submissions, 26%

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https://doi.org/10.4018/979-8-3693-0556-0.ch004
Yarozkaja LAleinikova D(2024)Lingvodidactic projection of the social and humanitarian sphere in the contour of artificial intelligence10.29039/978-5-94472-207-2-07-2024Online publication date: 8-Jul-2024
https://doi.org/10.29039/978-5-94472-207-2-07-2024
Pathak RSpezzano FBoratto LGena CMarras MGermanakos PPopescus E(2024)Analyzing the Interplay between Diversity of News Recommendations and Misinformation Spread in Social MediaAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664870(80-85)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664870
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Sacharidis D(2024)Fairness and Explainability for Enabling Trust in AI SystemsA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_3(85-110)Online publication date: 1-May-2024
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