Cluster-based quotas for fairness improvements in music recommendation systems

Wundervald, Bruna

doi:10.1007/s13735-020-00203-0

Cited by 14 publications

(8 citation statements)

References 15 publications

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“…Sometimes, the output of a "standard" recommender is taken as target distribution in evaluation of fairness e.g., for a "network-friendly" [129] or a coverage-aware recommender [125]. In some works, also simpler deviation measures are used, e.g., MAD [52,35,36] or GAP [76,130], which both share the characteristics to be point-wise (i.e., non-distributional) and not using a target representation. • Recommendation quality measures for groups: In a number of works on user-related fairness, the goal is to ensure that no group of users is discriminated by receiving recommendations of lower quality than another (privileged or majority) group.…”

Section: Methodsmentioning

confidence: 99%

“…Popularity Bias [77,36] Popularity Count [77] Exposure variance [105] Exposure/visibility gain [57] Exposure/relevance ratio [105] Weighted proportional fairness [73] Fraction of satisfied producers, inequality in exposures distribution [104] Popularity rate (and long-tail rate) [78] Gini index of: recommendation frequency [63]; Popularity scores [79] Ranking-based statistical parity, ranking-based equal-opportunity [127] Divergence or deviation based metrics KL divergence of exposure, normalized discounted KL divergence [72] Bias disparity [43,44,45] Generalized Cross Entropy [35] Max individual deviation, total variation distance, and KL-divergence (between distributions of item interactions) [129] Mean Absolute Difference (MAD) [52,35,36] Group Average Popularity (GAP) [76,130] User/item deviation cost [125] Disparity of exposure [68] Recommendation quality measures for groups NDCG (user groups) [117,59] F1 score [59] F-statistic [41] Relevance (item groups) [68] MAD-ranking [35] Recommendation quality measures for individuals…”

Section: Category Of Metrics Examplesmentioning

confidence: 99%

See 1 more Smart Citation

Fairness in Recommender Systems: Research Landscape and Future Directions

Deldjoo¹,

Jannach²,

Bellogín³

et al. 2022

Preprint

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Recommender systems can strongly influence which information we see online, e.g, on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterwards, we provide a survey of how research in this area is currently operationalized, for example, in terms of the general research methodology, fairness metrics, and algorithmic approaches. Overall, our analysis of recent works points to certain research gaps. In particular, we find that in many research works in computer science very abstract problem operationalizations are prevalent, which circumvent the fundamental and important question of what represents a fair recommendation in the context of a given application.

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Section: Methodsmentioning

confidence: 99%

Section: Category Of Metrics Examplesmentioning

confidence: 99%

Fairness in Recommender Systems: Research Landscape and Future Directions

Deldjoo¹,

Jannach²,

Bellogín³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…P-fairness Methods. Several works have studied recommendation fairness from the producer's perspective [39,40]. Gómez et al [19] assess recommender system algorithms disparate exposure based on producers' continent of production in movie and book recommendation domain and propose an equity-based approach to regulate the exposure of items produced in a continent.…”

Section: Background and Related Workmentioning

confidence: 99%

CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems

Naghiaei¹,

Rahmani²,

Deldjoo³

2022

Preprint

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Recently, there has been a rising awareness that when machine learning (ML) algorithms are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or economic consequences. Recommender systems are prominent examples of such ML systems that assist users in making high-stakes judgments.A common trend in the previous literature research on fairness in recommender systems is that the majority of works treat user and item fairness concerns separately, ignoring the fact that recommender systems operate in a two-sided marketplace. In this work, we present an optimization-based re-ranking approach that seamlessly integrates fairness constraints from both the consumer and producer-side in a joint objective framework. We demonstrate through large-scale experiments on 8 datasets that our proposed method is capable of improving both consumer and producer fairness without reducing overall recommendation quality, demonstrating the role algorithms may play in minimizing data biases. CCS CONCEPTS• Information systems → Recommender systems.

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“…Bruna Wundervald proposed a new method to predict music recommendation. By assuming that an artist's popularity distribution has a latent variable estimated by Gaussian mixture, the popularity clusters at the bottom are found and used to perform prediction by quota related to the mixing ratio of each cluster, so as to obtain the popularity distribution and make the music recommendation more accurately [4]. Saba Youse an Jazi et al proposed a music recommendation system to nd out users' favorite music through emotional perception, achieving high-precision music recommendation [5].…”

Section: Related Workmentioning

confidence: 99%

Multidimensional Analysis of Music Education System Based on Multi-Intelligent Recommendation

Wang

2022

Mobile Information Systems

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To better realize music education and improve the accuracy of music recommendation, a multidimensional analysis method of the music education system based on multi-intelligent recommendation is proposed. In the process of using this method to recommend music to users, the music characteristics are extracted and the music data are obtained from MIDI music, and three collaborative filtering algorithms are jointly used, namely user-based, content-based, and model-based collaborative filtering algorithms. The simulation results show that the proposed method can provide users with an intelligent music recommendation scheme according to the user’s basic information and operation information. Compared with a single user-based recommendation or content-based, model-based recommendation, the proposed method has a certain degree of novelty and accuracy. Here, the recommendation accuracy rate can reach 94.8%, which is higher than the other two recommendation algorithms, showing certain advantages.

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Cluster-based quotas for fairness improvements in music recommendation systems

Cited by 14 publications

References 15 publications

Fairness in Recommender Systems: Research Landscape and Future Directions

Fairness in Recommender Systems: Research Landscape and Future Directions

CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems

Multidimensional Analysis of Music Education System Based on Multi-Intelligent Recommendation

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