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Investigating gender fairness of recommendation algorithms in the music domain

Alessandro B. Melchiorre, Navid Rekabsaz, Emilia Parada-Cabaleiro, Stefan Brandl, Oleg Lesota, Markus Schedl
2021 Information Processing & Management  
Considering the importance of RSs in the distribution and consumption of musical content worldwide, a careful evaluation of fairness in the context of music RSs is crucial.  ...  and beyond-accuracy metrics to explore the fairness in the RS results toward a specific gender group.  ...  Acknowledgments This research is supported by Know-Center Graz, through project ''Theory-inspired Recommender Systems''. Appendix A.  ... 
doi:10.1016/j.ipm.2021.102666 fatcat:h5xa5oj5crdoxobk2z5n2fscru

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

Karlijn Dinnissen, Christine Bauer
2022 Frontiers in Big Data  
of artist gender in the recommendations.  ...  While there is an increasing interest in research on recommender system fairness in general, the music domain has received relatively little attention.  ...  Different from the movie domain, the size of the user group was not indicative of the recommender accuracy in the music domain.  ... 
doi:10.3389/fdata.2022.913608 pmid:35937551 pmcid:PMC9353048 fatcat:adjh25phcvh7nfomgquhbr2vei

Break the Loop: Gender Imbalance in Music Recommenders

Andres Ferraro, Xavier Serra, Christine Bauer
2021 Proceedings of the 2021 Conference on Human Information Interaction and Retrieval  
In interviews with music artists, we identified that gender fairness is one of the artists' main concerns. They emphasized that female artists should be given more exposure in music recommendations.  ...  For the evaluation, we rely on a simulation of feedback loops and provide an in-depth analysis using state-of-the-art performance measures and metrics concerning gender fairness.  ...  In the first part, we evaluate a recommendation algorithm that is widely used in the music domain with respect to gender fairness.  ... 
doi:10.1145/3406522.3446033 fatcat:in3u3q2tafeypkt3ybsrm2q2x4

Unfair Exposure of Artists in Music Recommendation [article]

Himan Abdollahpouri, Robin Burke, Masoud Mansoury
2020 arXiv   pre-print
In particular, fairness in recommender systems has been investigated to ensure the recommendations meet certain criteria with respect to certain sensitive features such as race, gender etc.  ...  In this paper, however, we investigate the impact of popularity bias in recommendation algorithms on the provider of the items (i.e. the entities who are behind the recommended items).  ...  Introduction Recommender systems have been widely used in a variety of different domains such as movies, music, online dating etc.  ... 
arXiv:2003.11634v1 fatcat:z6febc35bvezbece4da4nvj4oq

A Survey of Research on Fair Recommender Systems [article]

Yashar Deldjoo, Dietmar Jannach, Alejandro Bellogin, Alessandro Difonzo, Dario Zanzonelli
2022 arXiv   pre-print
a fair recommendation in the context of a given application.  ...  Afterward, 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.  ...  The by far most researched domain is the recommendation of videos (movies) and music, followed by e-commerce and finance.  ... 
arXiv:2205.11127v2 fatcat:qcq5iuwlevg2dh54i4on5jj4hi

Experiments on Generalizability of User-Oriented Fairness in Recommender Systems [article]

Hossein A. Rahmani, Mohammadmehdi Naghiaei, Mahdi Dehghan, Mohammad Aliannejadi
2022 arXiv   pre-print
the recommendation domain, nature of the base ranking model, and user grouping method.  ...  For instance, we see that the definition of the advantaged/disadvantaged user groups plays a crucial role in the effectiveness of the fairness algorithm and how it improves the performance of specific  ...  [13] propose a re-ranking algorithm for user-oriented fairness, where they only evaluate the performance of their method on one type of bias and domain i.e., gender and music, respectively.  ... 
arXiv:2205.08289v1 fatcat:ytaw34p22zhrxcb2kchgmabga4

Retrieval and Recommendation Systems at the Crossroads of Artificial Intelligence, Ethics, and Regulation

Markus Schedl, Emilia Gómez, Elisabeth Lex
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
This tutorial aims at providing its audience an interdisciplinary overview about the topics of fairness and non-discrimination, diversity, and transparency of AI systems, tailored to the research fields  ...  of information retrieval and recommender systems.  ...  (c) Algorithms to mitigate biases and improve fairness: We categorize the main strategies to mitigate harmful biases and improve fairness of retrieval and recommender systems, e.g., into pre-, in-, and  ... 
doi:10.1145/3477495.3532683 fatcat:dmq7dj4s35h6jjlfq3vgl3qrhi

Evaluating Music Recommendations with Binary Feedback for Multiple Stakeholders [article]

Sasha Stoikov, Hongyi Wen
2021 arXiv   pre-print
Using the Piki Music dataset of 500k ratings collected over a two-year time period, we evaluate the performance of classic recommendation algorithms on three important stakeholders: consumers, well-known  ...  Most existing music datasets suffer from noisy feedback and self-selection biases inherent in the data collected by music platforms.  ...  In the music domain, lesser-known artist have expressed many concerns, which include reaching an audience, transparency in recommendations, localizing discovery, gender balance and popularity bias, according  ... 
arXiv:2109.07692v1 fatcat:7ppjkcb5xncklguj36n7j3thum

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

Hossein A. Rahmani, Yashar Deldjoo, Ali Tourani, Mohammadmehdi Naghiaei
2022 arXiv   pre-print
We choose the POI recommendation as our test scenario; however, the insights should be trivially extendable on other domains.  ...  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.  ...  [20] attempted to investigate the user fairness in music recommendation on the dataset. The analysis of this paper is in line with the investigation of Abdollahpouri et al.  ... 
arXiv:2202.13307v2 fatcat:6tn7ho6sxvczdmtcowyorkinla

Humanized Recommender Systems: State-of-the-art and Research Issues

Thi Ngoc Trang Tran, Alexander Felfernig, Nava Tintarev
2021 ACM transactions on interactive intelligent systems (TiiS)  
These factors are also prevalent in the existing literature related to the inclusion of psychological aspects in recommender system development.  ...  In this article, we provide a rigorous review of existing research on the influence of the mentioned psychological factors on recommender systems.  ...  For instance, in the music domain, Rentfrow and Gosling [128] investigated how the music preferences of users are related to their personality.  ... 
doi:10.1145/3446906 fatcat:n6rsqmd5bzby7ipvqk4vmo5n24

User Acceptance of Gender Stereotypes in Automated Career Recommendations [article]

Clarice Wang, Kathryn Wang, Andrew Bian, Rashidul Islam, Kamrun Naher Keya, James Foulds, Shimei Pan
2021 arXiv   pre-print
While most research in this domain focuses on developing fair AI algorithms, in this work, we show that a fair AI algorithm on its own may be insufficient to achieve its intended results in the real world  ...  Currently, there is a surge of interest in fair Artificial Intelligence (AI) and Machine Learning (ML) research which aims to mitigate discriminatory bias in AI algorithms, e.g. along lines of gender,  ...  In the rest of the paper, we describe related literature, the implementation of a debiased machine learning algorithm for fair career recommendation, an offline evaluation of the system, an online user  ... 
arXiv:2106.07112v2 fatcat:bpycea6ikrarrkxeidp4phw7nq

Recommender Systems Fairness Evaluation via Generalized Cross Entropy [article]

Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogin, Tommaso Di Noia
2019 arXiv   pre-print
Fairness in recommender systems has been considered with respect to sensitive attributes of users (e.g., gender, race) or items (e.g., revenue in a multistakeholder setting).  ...  by allowing to incorporate domain knowledge (through an ideal fair distribution) that can help to understand which item or user aspects a recommendation algorithm is over- or under-representing.  ...  Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsors.  ... 
arXiv:1908.06708v1 fatcat:hz7bd6ynl5b4hb2jouclojx3di

Beyond Parity: Fairness Objectives for Collaborative Filtering [article]

Sirui Yao, Bert Huang
2017 arXiv   pre-print
We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data.  ...  We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness.  ...  Our running example of recommendation in education is inspired by the recent interest in using algorithms in this domain [5, 24, 27] .  ... 
arXiv:1705.08804v2 fatcat:fpnudfzjbvcsjaz5z6bq7zsbn4

Towards user-oriented privacy for recommender system data: A personalization-based approach to gender obfuscation for user profiles

Manel Slokom, Alan Hanjalic, Martha Larson
2021 Information Processing & Management  
Second, we carry out experiments that show that gender obfuscation impacts the fairness and diversity of recommender system results.  ...  In sum, our work establishes that a simple, transparent approach to gender obfuscation can protect user privacy while at the same time improving recommendation results for users by maintaining fairness  ...  Also, we would like to thank Christopher Strucks for the collaboration during his master thesis, which planted the seed for this work.  ... 
doi:10.1016/j.ipm.2021.102722 fatcat:o6szbxzukzdovpgomg77zdcqcu

Assessing Algorithmic Biases for Musical Version Identification [article]

Furkan Yesiler and Marius Miron and Joan Serrà and Emilia Gómez
2021 arXiv   pre-print
Version identification (VI) systems now offer accurate and scalable solutions for detecting different renditions of a musical composition, allowing the use of these systems in industrial applications and  ...  We find signs of disparities in identification performance for most of the groups we include in our analyses.  ...  In music technology research, recent works have addressed potential issues in music recommendation from both individual and group fairness perspectives [5, 17] by studying gender imbalance [8, 22]  ... 
arXiv:2109.15188v1 fatcat:4drrscwbcncszby4tuxx3vv34e
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