Personality Bias of Music Recommendation Algorithms
Abstract
Recommender systems, like other tools that make use of machine learning, are known to create or increase certain biases. Earlier work has already unveiled different performance of recommender systems for different user groups, depending on gender, age, country, and consumption behavior. In this work, we study user bias in terms of another aspect, i.e., users’ personality. We investigate to which extent state-of-the-art recommendation algorithms yield different accuracy scores depending on the users’ personality traits. We focus on the music domain and create a dataset of Twitter users’ music consumption behavior and personality traits, measuring the latter in terms of the OCEAN model. Investigating [email protected] and [email protected] of the recommendation algorithms SLIM, embarrassingly shallow autoencoders for sparse data (EASE), and variational autoencoders for collaborative filtering (Mult-VAE) on this dataset, we find several significant differences in performance between user groups scoring high vs. groups scoring low on several personality traits.
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The collaborative filtering-based recommender system approach can be problematic in terms of fairness and privacy.
“However, this approach can be problematic in terms of fairness and privacy [13-15, 18, 19].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [13]
The collaborative filtering-based recommender system approach can be problematic in terms of fairness and privacy.
“However, this approach can be problematic in terms of fairness and privacy [13-15, 18, 19].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [18]
Collaborative filtering-based approaches are more widely used than other recommender system categories.
“Among these, CF-based approaches are more widely used [9, 13-16].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [13]
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- Authors
- Melchiorre, A. B., Zangerle, E., & Schedl, M.
- Affiliations
- Johannes Kepler University of Linz (AT)
- Universität Innsbruck (AT)
- Venue
- Fourteenth ACM Conference on Recommender Systems
- Publisher
- ACM
- Published
- 2020-09-22
- Pages
- 533-538
- OpenAlex
- W3088329504
- Cited by
- 48
- References
- 29
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 10 · 2023: 10 · 2022: 12 · 2021: 4
- FWCI
- 4.93
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Conference on Recommender Systems (RecSys) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Keywords
- music recommendation, recommender systems, algorithmic bias, personality traits, ocean model, user heterogeneity, Twitter users, collaborative filtering, SLIM, ease, recommendation accuracy
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