Calibration in Collaborative Filtering Recommender Systems: a User-Centered Analysis
Recommender systems learn from past user preferences in order to predict future user interests and provide users with personalized suggestions. Previous research has demonstrated that biases in user profiles in the aggregate can influence the recommendations to users who do not share the majority preference. One consequence of this bias propagation effect is miscalibration, a mismatch between the types or categories of items that a user prefers and the items provided in recommendations. In this paper, we conduct a systematic analysis aimed at identifying key characteristics in user profiles...- doi
- 10.1145/3372923.3404793
- name
- Calibration in Collaborative Filtering Recommender Systems: a User-Centered Analysis
- pages
- 10
- acm_url
- https://dl.acm.org/doi/10.1145/3372923.3404793
- authors
- Kun Lin, Nasim Sonboli, Bamshad Mobasher, Robin Burke
- doi_url
- https://doi.org/10.1145/3372923.3404793
- license
- restricted
- summary
- Recommender systems learn from past user preferences in order to predict future user interests and provide users with personalized suggestions. Previous research has demonstrated that biases in user profiles in the aggregate can influence the recommendations to users who do not share the majority preference. One consequence of this bias propagation effect is miscalibration, a mismatch between the types or categories of items that a user prefers and the items provided in recommendations. In this paper, we conduct a systematic analysis aimed at identifying key characteristics in user profiles...
- keywords
- Calibration; Algorithmic Bias; Bias Amplification; Fairness; Recommender systems
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- HT-2020_51-35_3372923/3372923.3404793.pdf
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- ccs_concepts
- • Information systems → Recommender systems; Collaborative filtering;
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- Kun Lin, Nasim Sonboli, Bamshad Mobasher, Robin Burke
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- a541f14c81378123900a4ab1a09bde14827aef264919f7f65326e438c0c3d03c
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- Formatting converted from the ACM version of record under supplied ACM publication authorization.
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- v4 passed: source, body, figures, tables, equations, references, and native IPFS assets verified
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