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ORCIDBIAS 2022: Stavanger, Norway
- Ludovico Boratto
, Stefano Faralli, Mirko Marras, Giovanni Stilo:
Advances in Bias and Fairness in Information Retrieval - Third International Workshop, BIAS 2022, Stavanger, Norway, April 10, 2022, Revised Selected Papers. Communications in Computer and Information Science 1610, Springer 2022, ISBN 978-3-031-09315-9 - Dominik Kowald, Emanuel Lacic:
Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems. 1-11 - Naieme Hazrati, Francesco Ricci:
The Impact of Recommender System and Users' Behaviour on Choices' Distribution and Quality. 12-20 - Ali Shirali:
Sequential Nature of Recommender Systems Disrupts the Evaluation Process. 21-34 - Julia Neidhardt, Mete Sertkan:
Towards an Approach for Analyzing Dynamic Aspects of Bias and Beyond-Accuracy Measures. 35-42 - Binh Le, Damiano Spina, Falk Scholer, Hui Xian Chia:
A Crowdsourcing Methodology to Measure Algorithmic Bias in Black-Box Systems: A Case Study with COVID-Related Searches. 43-55 - Hossein A. Rahmani, Yashar Deldjoo, Ali Tourani, Mohammadmehdi Naghiaei:
The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation. 56-68 - Mohammadmehdi Naghiaei, Hossein A. Rahmani, Mahdi Dehghan:
The Unfairness of Popularity Bias in Book Recommendation. 69-81 - Anastasiia Klimashevskaia, Mehdi Elahi, Dietmar Jannach, Christoph Trattner, Lars Skjærven:
Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches. 82-90 - Carlos Rojas, David Contreras, Maria Salamó:
Analysis of Biases in Calibrated Recommendations. 91-103 - Klara Krieg, Emilia Parada-Cabaleiro, Markus Schedl, Navid Rekabsaz:
Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements? 104-116 - Giordano d'Aloisio, Giovanni Stilo, Antinisca Di Marco, Andrea D'Angelo:
Enhancing Fairness in Classification Tasks with Multiple Variables: A Data- and Model-Agnostic Approach. 117-129 - Harshit Mishra, Sucheta Soundarajan:
Keyword Recommendation for Fair Search. 130-142 - Davide Azzalini, Elisa Quintarelli, Emanuele Rabosio, Letizia Tanca:
FARGO: A Fair, Context-AwaRe, Group RecOmmender System. 143-154
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