Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?

Lesota, Oleg; Melchiorre, Alessandro B.; Rekabsaz, Navid; Brandl, Stefan; Kowald, Dominik; Lex, Elisabeth; Schedl, Markus

doi:10.1145/3460231.3478843

Cited by 45 publications

(9 citation statements)

References 15 publications

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“…Zooming in on another user characteristic, several studies investigate gender. They show that popularity bias particularly affects minority gender groups (in these studies: women), resulting in lower-quality recommendations in terms of accuracy and coverage (e.g., Lesota et al, 2021;Melchiorre et al, 2021). In addition to finding similar results for user gender, Ekstrand et al (2018) and its reproducibility study by Neophytou et al (2022) found performance differences for different user age groups, too.…”

Section: User Perspectivementioning

confidence: 73%

Fairness in Music Recommender Systems: A Stakeholder-Centered Mini Review

Dinnissen

Bauer

2022

Front. Big Data

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The performance of recommender systems highly impacts both music streaming platform users and the artists providing music. As fairness is a fundamental value of human life, there is increasing pressure for these algorithmic decision-making processes to be fair as well. However, many factors make recommender systems prone to biases, resulting in unfair outcomes. Furthermore, several stakeholders are involved, who may all have distinct needs requiring different fairness considerations. While there is an increasing interest in research on recommender system fairness in general, the music domain has received relatively little attention. This mini review, therefore, outlines current literature on music recommender system fairness from the perspective of each relevant stakeholder and the stakeholders combined. For instance, various works address gender fairness: one line of research compares differences in recommendation quality across user gender groups, and another line focuses on the imbalanced representation of artist gender in the recommendations. In addition to gender, popularity bias is frequently addressed; yet, primarily from the user perspective and rarely addressing how it impacts the representation of artists. Overall, this narrative literature review shows that the large majority of works analyze the current situation of fairness in music recommender systems, whereas only a few works propose approaches to improve it. This is, thus, a promising direction for future research.

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Section: User Perspectivementioning

confidence: 73%

Fairness in Music Recommender Systems: A Stakeholder-Centered Mini Review

Dinnissen

Bauer

2022

Front. Big Data

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“…Parallel to the former study, these studies also found that the users' interests in popular items highly differ, and individuals interested in long‐tail items receive more unfair recommendations than others for both domains. Also, another related study 17 has analyzed the discrepancies between the popularity of items in the profiles of users and the produced recommendation lists for users of different genders and concluded that females are more affected by such algorithmic popularity bias in music recommendations compared to males. Besides, another recent research 15 has considered various discriminative attributes related to rating behaviors of users, such as profile size, profile mean, profile deviation, profile anomaly, and profile entropy, and analyzed their potential relations with the unfairness propagation of the recommender systems.…”

Section: Related Workmentioning

confidence: 99%

“…A few recent studies have reperformed such analyses for users in protected classes, such as age 16 or gender. 17 However, keeping in mind that personality traits are critical elements for understanding the behavior of individuals, to the best of our knowledge, how users' popularity inclination in their original profiles varies concerning their personality characteristics has not yet been examined in the literature. More importantly, how popularity bias affects individuals with different characteristics is still elusive.…”

Section: Problem Statement and Motivationmentioning

confidence: 99%

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Popularity bias in personality perspective: An analysis of how personality traits expose individuals to the unfair recommendation

Yalçin

Bilge

2023

Concurrency and Computation

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Recommender systems are subject to well-known popularity bias issues, that is, they expose frequently rated items more in recommendation lists than less-rated ones. Such a problem could also have varying effects on users with different gender, age, or rating behavior, which significantly diminishes the users' overall satisfaction with recommendations. In this paper, we approach the problem from the view of user personalities for the first time and discover how users are inclined toward popular items based on their personality traits. More importantly, we analyze the potential unfairness concerns for users with different personalities, which the popularity bias of the recommenders might cause. To this end, we split users into groups of high, moderate, and low clusters in terms of each personality trait in the big-five factor model and investigate how the popularity bias impacts such groups differently by considering several criteria. The experiments conducted with 10 well-known algorithms of different kinds have concluded that less-extroverted people and users avoiding new experiences are exposed to more unfair recommendations regarding popularity, despite being the most significant contributors to the system. However, discrepancies in other qualities of the recommendations for these user characteristics, such as accuracy, diversity, and novelty, vary depending on the utilized algorithm.

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“…Their experiment showed that unfairness in location recommender systems is likely related to location popularity or inclination toward some user groups based on nationality. Lesota et al [21] investigated item popularity fairness in the music domain using the LFM-2b 6 dataset, i.e., P-fairness, and addressed shortcomings of the statistical analysis in fairness by considering the distribution between user-profiles and recommendation lists. Furthermore, they analyzed whether such algorithmic popularity bias affects users of different genders.…”

Section: Related Workmentioning

confidence: 99%

The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation

Rahmani¹,

Deldjoo²,

Tourani³

et al. 2022

Preprint

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Point-of-Interest (POI) recommender systems provide personalized recommendations to users and help businesses attract potential customers. Despite their success, recent studies suggest that highly datadriven recommendations could be impacted by data biases, resulting in unfair outcomes for different stakeholders, mainly consumers (users) and providers (items). Most existing fairness-related research works in recommender systems treat user fairness and item fairness issues individually, disregarding that RS work in a two-sided marketplace. This paper studies the interplay between (i) the unfairness of active users, (ii) the unfairness of popular items, and (iii) the accuracy (personalization) of recommendation as three angles of our study triangle. We group users into advantaged and disadvantaged levels to measure user fairness based on their activity level. For item fairness, we divide items into short-head, mid-tail, and long-tail groups and study the exposure of these item groups into the top-k recommendation list of users. Experimental validation of eight different recommendation models commonly used for POI recommendation (e.g., contextual, CF) on two publicly available POI recommendation datasets, Gowalla and Yelp, indicate that most well-performing models suffer seriously from the unfairness of popularity bias (provider unfairness). Furthermore, our study shows that most recommendation models cannot satisfy both consumer and producer fairness, indicating a trade-off between these variables possibly due to natural biases in data. We choose the POI recommendation as our test scenario; however, the insights should be trivially extendable on other domains.

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Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?

Cited by 45 publications

References 15 publications

Fairness in Music Recommender Systems: A Stakeholder-Centered Mini Review

Fairness in Music Recommender Systems: A Stakeholder-Centered Mini Review

Popularity bias in personality perspective: An analysis of how personality traits expose individuals to the unfair recommendation

The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation

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