Titlebook: Advances in Bias and Fairness in Information Retrieval; Third International Ludovico Boratto,Stefano Faralli,Giovanni Stilo Conference pro

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,Enhancing Fairness in?Classification Tasks with?Multiple Variables: A Data- and Model-Agnostic Apprn daran, dass sie nicht zufrieden stellend erk- ren k?nnen, warum Lernen dermassen erfolgreich ist, wenn es um die Wahl optimaler Handlungs- und Verhaltenseisen in konkreten Situationen geht. Nicht das (kognitive) F978-3-531-16374-1978-3-531-91312-4

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,The Impact of?Recommender System and?Users’ Behaviour on?Choices’ Distribution and?Quality,978-3-531-94185-1

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FARGO: A Fair, Context-AwaRe, Group RecOmmender System,978-3-322-87566-2

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Advances in Bias and Fairness in Information RetrievalThird International

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Conclusion: A Short-Form Future?large user profiles and thus, are important data sources for multimedia recommender systems. Secondly, we find that popular items are recommended more frequently than unpopular ones. Thirdly, we find that users with little interest into popular items receive significantly worse recommendations than

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Conclusion: A Short-Form Future?sults for the same queries depending on the country in which they reside. These results indicate that using crowdsourcing platforms to study system behavior, in a way that preserves participant privacy, is a viable approach to obtain insights into black-box systems, supporting research investigation

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