Trustworthy Algorithmic Ranking Systems

Author:

Schedl Markus1ORCID,Gómez Emilia2ORCID,Lex Elisabeth3ORCID

Affiliation:

1. Johannes Kepler University Linz & Linz Institute of Technology, Linz, Austria

2. European Commission, Joint Research Centre & Universitat Pompeu Fabra, Seville and Barcelona, Spain

3. Graz University of Technology, Graz, Austria

Funder

Austrian Science Fund (FWF)

State of Upper Austria and the Federal Ministry of Education, Science, and Research

European Commission, Digital Economy Unit, Joint Research Centre

Publisher

ACM

Reference44 articles.

1. Explainability in music recommender systems

2. The algorithm audit: Scoring the algorithms that score us

3. Peter Brusilovsky , Marco de Gemmis , Alexander Felfernig , Elisabeth Lex , Pasquale Lops , Giovanni Semeraro , and Martijn C. Willemsen . 2021 . Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'21) . In Fifteenth ACM Conference on Recommender Systems (Amsterdam, Netherlands) (RecSys '21). 783--786. https://doi.org/10.1145/3460231.3470927 10.1145/3460231.3470927 Peter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Elisabeth Lex, Pasquale Lops, Giovanni Semeraro, and Martijn C. Willemsen. 2021. Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'21). In Fifteenth ACM Conference on Recommender Systems (Amsterdam, Netherlands) (RecSys '21). 783--786. https://doi.org/10.1145/3460231.3470927

4. Preface to the special issue on fair, accountable, and transparent recommender systems

5. A flexible framework for evaluating user and item fairness in recommender systems

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4. Trustworthy Recommender Systems: Technical, Ethical, Legal, and Regulatory Perspectives;Proceedings of the 17th ACM Conference on Recommender Systems;2023-09-14

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