Advances in Bias and Fairness in Information Retrieval : Third International Workshop, BIAS 2022, Stavanger, Norway, April 10, 2022, Revised Selected Papers / edited by Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo.

Format:

Book

Contributor:

Boratto, Ludovico, Editor.

Faralli, Stefano, Editor.

Marras, Mirko., Editor.

Stilo, Giovanni., Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Communications in computer and information science 1865-0937 ; 1610

Communications in Computer and Information Science, 1865-0937 ; 1610

Language:

English

Subjects (All):

Computer engineering.

Computer networks.

Artificial intelligence.

Electronic commerce.

Computer Engineering and Networks.

Artificial Intelligence.

e-Commerce and e-Business.

Local Subjects:

Computer Engineering and Networks.

Artificial Intelligence.

e-Commerce and e-Business.

Physical Description:

1 online resource (X, 155 pages) : 35 illustrations, 30 illustrations in color.

Edition:

1st ed. 2022.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2022.

System Details:

text file PDF

Summary:

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. .

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-031-09316-6

9783031093166

Access Restriction:

Restricted for use by site license.