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Advances in Bias and Fairness in Information Retrieval

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Advances in Bias and Fairness in Information Retrieval
Springer International Publishing

Table of Contents

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    Book Overview
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    Chapter 1 Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems
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    Chapter 2 The Impact of Recommender System and Users’ Behaviour on Choices’ Distribution and Quality
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    Chapter 3 Sequential Nature of Recommender Systems Disrupts the Evaluation Process
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    Chapter 4 Towards an Approach for Analyzing Dynamic Aspects of Bias and Beyond-Accuracy Measures
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    Chapter 5 A Crowdsourcing Methodology to Measure Algorithmic Bias in Black-Box Systems: A Case Study with COVID-Related Searches
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    Chapter 6 The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation
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    Chapter 7 The Unfairness of Popularity Bias in Book Recommendation
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    Chapter 8 Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches
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    Chapter 9 Analysis of Biases in Calibrated Recommendations
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    Chapter 10 Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements?
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    Chapter 11 Enhancing Fairness in Classification Tasks with Multiple Variables: A Data- and Model-Agnostic Approach
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    Chapter 12 Keyword Recommendation for Fair Search
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    Chapter 13 FARGO: A Fair, Context-AwaRe, Group RecOmmender System
Attention for Chapter 8: Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches
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