paper-with-me

Papers

A Stakeholder-Centered View on Fairness in Music Recommender Systems

2022-09-08 · Karlijn Dinnissen, Christine Bauer

Our narrative literature review acknowledges that, although there is an increasing interest in recommender system fairness in general, the music domain has received relatively little attention in this regard. However, addressing fairness of music recommender systems (MRSs) is highly important because the performance of these systems considerably impacts both the users of music streaming platforms and the artists providing music to those platforms. The distinct needs that these stakeholder groups may have, and the different aspects of fairness that therefore should be considered, make for a challenging research field with ample opportunities for improvement. The review first outlines current literature on MRS fairness from the perspective of each stakeholder and the stakeholders combined, and then identifies promising directions for future research. The two open questions arising from the review are as follows: (i) In the MRS field, only limited data is publicly available to conduct fairness research; most datasets either originate from the same source or are proprietary (and, thus, not widely accessible). How can we address this limited data availability? (ii) Overall, the review shows that the large majority of works analyze the current situation of MRS fairness, whereas only few works propose approaches to improve it. How can we move forward to a focus on improving fairness aspects in these recommender systems? At FAccTRec '22, we emphasize the specifics of addressing RS fairness in the music domain.

📄 PDF Abstract BibTeX arXiv:2209.06126

Code (0)

등록된 구현이 없습니다.

Tasks

FairnessRecommendation Systems

Similar Papers 제목 키워드 기반

It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation

2024-08-22 · Andres Ferraro, Michael D. Ekstrand, Christine Bauer

As recommender systems are prone to various biases, mitigation approaches are needed to ensure that recommendations are fair to various stakeholders. One particular concern in music recommendation is artist gender fairne…

FairnessMusic RecommendationRecommendation SystemsRe-Ranking

Unfair Exposure of Artists in Music Recommendation

2020-03-25 · Himan Abdollahpouri, Robin Burke, Masoud Mansoury

Fairness in machine learning has been studied by many researchers. In particular, fairness in recommender systems has been investigated to ensure the recommendations meet certain criteria with respect to certain sensitiv…

FairnessMusic RecommendationRecommendation Systems

EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders

2024-07-16 · Lin Luo, Yuri Nakao, Mathieu Chollet, Hiroya Inakoshi 외

Numerous fairness metrics have been proposed and employed by artificial intelligence (AI) experts to quantitatively measure bias and define fairness in AI models. Recognizing the need to accommodate stakeholders' diverse…

Fairness

Towards Individual and Multistakeholder Fairness in Tourism Recommender Systems

2023-09-05 · Ashmi Banerjee, Paromita Banik, Wolfgang Wörndl

This position paper summarizes our published review on individual and multistakeholder fairness in Tourism Recommender Systems (TRS). Recently, there has been growing attention to fairness considerations in recommender s…

FairnessRecommendation Systems

Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness

2019-07-30 · Himan Abdollahpouri, Robin Burke

There is growing research interest in recommendation as a multi-stakeholder problem, one where the interests of multiple parties should be taken into account. This category subsumes some existing well-established areas o…

FairnessRecommendation Systems