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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 1st ed. 2022 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 155 pages, height x width: 235x155 mm, weight: 267 g, 30 Illustrations, color; 5 Illustrations, black and white; X, 155 p. 35 illus., 30 illus. in color., 1 Paperback / softback
  • Sērija : Communications in Computer and Information Science 1610
  • Izdošanas datums: 19-Jun-2022
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031093151
  • ISBN-13: 9783031093159
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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 1st ed. 2022Palielināt
  • Formāts: Paperback / softback, 155 pages, height x width: 235x155 mm, weight: 267 g, 30 Illustrations, color; 5 Illustrations, black and white; X, 155 p. 35 illus., 30 illus. in color., 1 Paperback / softback
  • Sērija : Communications in Computer and Information Science 1610
  • Izdošanas datums: 19-Jun-2022
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031093151
  • ISBN-13: 9783031093159
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031093159.html
Keywords:
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. 
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.