Advances in Bias and Fairness in Information Retrieval

Third International Workshop, BIAS 2022, Stavanger, Norway, April 10, 2022, Revised Selected Papers
Herausgegeben:Boratto, Ludovico; Faralli, Stefano; Marras, Mirko; Stilo, Giovanni

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Communications in Computer and Information Science 1610
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-09315-9
• 1st ed. 2022
• Seitenzahl: 168
• Erscheinungstermin: 19. Juni 2022
• Englisch
• Abmessung: 235mm x 155mm x 10mm
• Gewicht: 265g
• ISBN-13: 9783031093159
• ISBN-10: 3031093151
• Artikelnr.: 64065304

Inhaltsangabe

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.