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Advances in Bias and Fairness in Information Retrieval

Third International Workshop, BIAS 2022, Stavanger, Norway, April 10, 2022, Revised Selected Papers
BuchKartoniert, Paperback
155 Seiten
Englisch
Springererschienen am19.06.20221st ed. 2022
This book constitutes refereed proceedings of the Third International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2022, held in April, 2022.mehr
Verfügbare Formate
BuchKartoniert, Paperback
EUR53,49
BuchKartoniert, Paperback
EUR69,54
BuchKartoniert, Paperback
EUR69,54
E-BookPDF1 - PDF WatermarkE-Book
EUR69,54

Produkt

KlappentextThis book constitutes refereed proceedings of the Third International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2022, held in April, 2022.
Details
ISBN/GTIN978-3-031-09315-9
ProduktartBuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2022
Erscheinungsdatum19.06.2022
Auflage1st ed. 2022
ReiheCommunications in Computer and Information Science
Seiten155 Seiten
SpracheEnglisch
IllustrationenX, 155 p. 35 illus., 30 illus. in color.
Artikel-Nr.16553064

Inhalt/Kritik

Inhaltsverzeichnis
Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems.- Recommender Systems and Users' Behaviour Effect on Choice's Distribution and Quality.- Sequential Nature of Recommender Systems Disrupts the Evaluation Process.- Towards an Approach for Analyzing Dynamic Aspects of Bias and Beyond-Accuracy Measures.- A Crowdsourcing Methodology to Measure Algorithmic Bias in Black-box Systems: A Case Study with COVID-related Searches.- The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation.- The Unfairness of Popularity Bias in Book Recommendation.- Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches.- Analysis of Biases in Calibrated Recommendations.- Do Perceived Gender Biases in Retrieval Results affect Users´ Relevance Judgements?.- Enhancing Fairness in Classification Tasks with Multiple Variables: a Data- and Model-Agnostic Approach.- Keyword Recommendation for Fair Search.- FARGO: a Fair, context-AwaRe, Group recOmmender system.mehr

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