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Beyond-Accuracy Provider Fairness (Slides)

Hossein A. (Saeed) Rahmani
Hossein A. (Saeed) Rahmani
Hossein A. (Saeed) RahmaniPhD Student at University College London

Recommender systems, while transformative in online user experiences, have raised concerns over potential provider-side fairness issues. These systems may inadvertently favor popular items, thereby marginalizing less popular ones and compromising provider fairness. While previous research has recognized provider-side fairness issues, the investigation into how these biases affect beyond-accuracy aspects of recommendation systems - such as diversity, novelty, coverage, and serendipity - has been less emphasized. In this paper, we address this gap by introducing a simple yet effective post-processing re-ranking model that prioritizes provider fairness, while simultaneously maintaining user relevance and recommendation quality. We then conduct an in-depth evaluation of the model's impact on various aspects of recommendation quality across multiple datasets. Specifically, we apply the post-processing algorithm to four distinct recommendation models across four varied domain datasets, assessing the improvement in each metric, encompassing both accuracy and beyond-accuracy aspects. This comprehensive analysis allows us to gauge the effectiveness of our approach in mitigating provider biases. Our findings underscore the effectiveness of the adopted method in improving provider fairness and recommendation quality. They also provide valuable insights into the trade-offs involved in achieving fairness in recommender systems, contributing to a more nuanced understanding of this complex issue.

Beyond-Accuracy Provider Fairness (Slides)

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Provider Fairness and Beyond-Accuracy Trade-offs
in Recommender Systems
Saeedeh Karimi1
, Hossein A. Rahmani2
, Mohammadmehdi Naghiaei3
, Leila Safari1
1
University of Zanjan, Iran
2
University College London, UK
3
University of Southern California, US
The 6th FAccTRec Workshop: Responsible Recommendation
The 17th ACM Conference on Recommender Systems
Singapore, 18th-22nd September 2023
Introduction
● Recommendation systems often suffer from popularity bias, a phenomenon
where popular items are disproportionately recommended at the expense of
less popular or long-tail items
● Popularity bias leads to a skewed exposure of items and potentially unfair
treatment of providers, particularly those offering niche or less popular
content.
● Popularity bias may adversely affect the diversity, novelty, and serendipity of
recommendations, thereby limiting users’ experiences and the discovery of new
content.
Contribution
● Past research in recommendation systems mainly emphasized accuracy over
fairness considerations.
● Our study explores the trade-offs involved in enhancing fairness in
recommendation systems.
● We introduce a method for improving fairness while preserving accuracy,
inspired by post-processing techniques.
● We extensively analyze the trade-offs between provider fairness and
recommendation quality across four algorithms and datasets in various
domains.
Proposed Method
Datasets
Results

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Beyond-Accuracy Provider Fairness (Slides)

  • 1. Provider Fairness and Beyond-Accuracy Trade-offs in Recommender Systems Saeedeh Karimi1 , Hossein A. Rahmani2 , Mohammadmehdi Naghiaei3 , Leila Safari1 1 University of Zanjan, Iran 2 University College London, UK 3 University of Southern California, US The 6th FAccTRec Workshop: Responsible Recommendation The 17th ACM Conference on Recommender Systems Singapore, 18th-22nd September 2023
  • 2. Introduction ● Recommendation systems often suffer from popularity bias, a phenomenon where popular items are disproportionately recommended at the expense of less popular or long-tail items ● Popularity bias leads to a skewed exposure of items and potentially unfair treatment of providers, particularly those offering niche or less popular content. ● Popularity bias may adversely affect the diversity, novelty, and serendipity of recommendations, thereby limiting users’ experiences and the discovery of new content.
  • 3. Contribution ● Past research in recommendation systems mainly emphasized accuracy over fairness considerations. ● Our study explores the trade-offs involved in enhancing fairness in recommendation systems. ● We introduce a method for improving fairness while preserving accuracy, inspired by post-processing techniques. ● We extensively analyze the trade-offs between provider fairness and recommendation quality across four algorithms and datasets in various domains.
  • 9. Thanks! Saeedeh Karimi (karimi.saeedeh@znu.ac.ir) Hossein A. Rahmani (hossein.rahmani.22@ucl.ac.uk) Mohammadmehdi Naghiaei (naghiaei@usc.edu) Leila Safari (lsafari@znu.ac.ir) Code: https://github.com/rahmanidashti/BeyondAccProvider Do you have any questions?