As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing, researchers have begun to seek ways to ensure fairness in the results that such systems produce. This work has primarily focused on developing recommendation approaches in which fairness metrics are jointly optimized along with recommendation accuracy. However, the previous work had largely ignored how individual preferences may limit the ability of an algorithm to produce fair recommendations. Furthermore, with few exceptions, researchers have only considered scenarios in which fairness is measured relative to a single sensitive feature or attribute (such as race or gender). In this paper, we present a re-ranking approach to fairness-aware recommendation that learns individual preferences across multiple fairness dimensions and uses them to enhance provider fairness in recommendation results. Specifically, we show that our opportunistic and metric-agnostic approach achieves a better trade-off between accuracy and fairness than prior re-ranking approaches and does so across multiple fairness dimensions.
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This content will become publicly available on March 25, 2025
Towards Optimizing Ranking in Grid-Layout for Provider-side Fairness
Information access systems, such as search engines and recommender systems,
order and position results based on their estimated relevance. These results are
then evaluated for a range of concerns, including provider-side fairness:
whether exposure to users is fairly distributed among items and the people who
created them. Several fairness-aware ranking and re-ranking techniques have been
proposed to ensure fair exposure for providers, but this work focuses almost
exclusively on linear layouts in which items are displayed in single ranked
list. Many widely-used systems use other layouts, such as the grid views common
in streaming platforms, image search, and other applications. Providing fair
exposure to providers in such layouts is not well-studied. We seek to fill this
gap by providing a grid-aware re-ranking algorithm to optimize layouts for
provider-side fairness by adapting existing re-ranking techniques to grid-aware
browsing models, and an analysis of the effect of grid-specific factors such as
device size on the resulting fairness optimization. Our work provides a starting
point and identifies open gaps in ensuring provider-side fairness in grid-based
layouts.
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- Award ID(s):
- 2415042
- NSF-PAR ID:
- 10497109
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- ECIR 2024: Advances in Information Retrieval
- Volume:
- 14612
- Page Range / eLocation ID:
- 90-105
- Subject(s) / Keyword(s):
- ["recommender systems","provider fairness","reranking"]
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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