Advances in Bias and Fairness in Information Retrieval

Chapter 1 Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems

Chapter 2 The Impact of Recommender System and Users’ Behaviour on Choices’ Distribution and Quality

Chapter 3 Sequential Nature of Recommender Systems Disrupts the Evaluation Process

Chapter 4 Towards an Approach for Analyzing Dynamic Aspects of Bias and Beyond-Accuracy Measures

Chapter 5 A Crowdsourcing Methodology to Measure Algorithmic Bias in Black-Box Systems: A Case Study with COVID-Related Searches

Chapter 6 The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation

Chapter 8 Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches

Chapter 9 Analysis of Biases in Calibrated Recommendations

Chapter 10 Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements?

Chapter 11 Enhancing Fairness in Classification Tasks with Multiple Variables: A Data- and Model-Agnostic Approach

Chapter 12 Keyword Recommendation for Fair Search

Chapter 13 FARGO: A Fair, Context-AwaRe, Group RecOmmender System