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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 de Ludovico Boratto

Advances in Bias and Fairness in Information Retrieval: Third International Workshop, BIAS 2022, Stavanger, Norway, April 10, 2022, Revised Selected Papers: Communications in Computer and Information Science, cartea 1610

Editat de Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo
en Limba Engleză Paperback – 19 iun 2022
This book constitutes refereed proceedings of the Third International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2022, held in April, 2022. 

The 9 full papers and 4 short papers were carefully reviewed and selected from 34 submissions. The papers cover topics that go from search and recommendation in online dating, education, and social media, over the impact of gender bias in word embeddings, to tools that allow to explore bias and fairnesson the Web. 
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Specificații

ISBN-13: 9783031093159
ISBN-10: 3031093151
Pagini: 155
Ilustrații: X, 155 p. 35 illus., 30 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.25 kg
Ediția:1st ed. 2022
Editura: Springer International Publishing
Colecția Springer
Seria Communications in Computer and Information Science

Locul publicării:Cham, Switzerland

Cuprins

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.