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Publication search results
found 62 matches
- 2023
- Vivek Kumar, Giacomo Medda, Diego Reforgiato Recupero, Daniele Riboni, Rim Helaoui, Gianni Fenu:
How Do You Feel? Information Retrieval in Psychotherapy and Fair Ranking Assessment. BIAS 2023: 119-133 - Nicola Alimonda, Alessandro Castelnovo, Riccardo Crupi, Fabio Mercorio, Mario Mezzanzanica:
Preserving Utility in Fair Top-k Ranking with Intersectional Bias. BIAS 2023: 59-73 - Sheetal Borar, Hilde J. P. Weerts, Binyam Gebre, Mykola Pechenizkiy:
Improving Recommender System Diversity with Variational Autoencoders. BIAS 2023: 85-99 - Fouzi Harrag, Chaima Mahdadi, Amina Nourhane Ziad:
Detecting and Measuring Social Bias of Arabic Generative Models in the Context of Search and Recommendation. BIAS 2023: 155-168 - Zahid Irfan, Fergal McCaffery, Róisín Loughran:
Evaluating Fairness Metrics. BIAS 2023: 31-41 - Dominik Kowald, Gregor Mayr, Markus Schedl, Elisabeth Lex:
A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations. BIAS 2023: 1-16 - Yinxiao Li:
Mitigating Position Bias in Hotels Recommender Systems. BIAS 2023: 74-84 - Hamed Loghmani, Hossein Fani:
Bootless Application of Greedy Re-ranking Algorithms in Fair Neural Team Formation. BIAS 2023: 108-118 - Abhishek Mandal, Susan Leavy, Suzanne Little:
Measuring Bias in Multimodal Models: Multimodal Composite Association Score. BIAS 2023: 17-30 - Erasmo Purificato, Ernesto William De Luca:
What Are We Missing in Algorithmic Fairness? Discussing Open Challenges for Fairness Analysis in User Profiling with Graph Neural Networks. BIAS 2023: 169-175 - Shaina Raza, Syed Raza Bashir, Sneha, Urooj Qamar:
Addressing Biases in the Texts Using an End-to-End Pipeline Approach. BIAS 2023: 100-107 - Bruno Scarone, Ricardo Baeza-Yates, Erik Bernhardson:
Understanding Search Behavior Bias in Wikipedia. BIAS 2023: 134-146 - Sanne Vrijenhoek:
Do You MIND? Reflections on the MIND Dataset for Research on Diversity in News Recommendations. BIAS 2023: 147-154 - Kuanyi Zhang, Min Xie, Yi Zhang, Haixian Zhang:
Utilizing Implicit Feedback for User Mainstreaminess Evaluation and Bias Detection in Recommender Systems. BIAS 2023: 42-58 - Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo:
Advances in Bias and Fairness in Information Retrieval - 4th International Workshop, BIAS 2023, Dublin, Ireland, April 2, 2023, Revised Selected Papers. Communications in Computer and Information Science 1840, Springer 2023, ISBN 978-3-031-37248-3 [contents] - 2022
- Davide Azzalini, Elisa Quintarelli, Emanuele Rabosio, Letizia Tanca:
FARGO: A Fair, Context-AwaRe, Group RecOmmender System. BIAS 2022: 143-154 - Naieme Hazrati, Francesco Ricci:
The Impact of Recommender System and Users' Behaviour on Choices' Distribution and Quality. BIAS 2022: 12-20 - Anastasiia Klimashevskaia, Mehdi Elahi, Dietmar Jannach, Christoph Trattner, Lars Skjærven:
Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches. BIAS 2022: 82-90 - Dominik Kowald, Emanuel Lacic:
Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems. BIAS 2022: 1-11 - Klara Krieg, Emilia Parada-Cabaleiro, Markus Schedl, Navid Rekabsaz:
Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements? BIAS 2022: 104-116 - Binh Le, Damiano Spina, Falk Scholer, Hui Chia:
A Crowdsourcing Methodology to Measure Algorithmic Bias in Black-Box Systems: A Case Study with COVID-Related Searches. BIAS 2022: 43-55 - Harshit Mishra, Sucheta Soundarajan:
Keyword Recommendation for Fair Search. BIAS 2022: 130-142 - Mohammadmehdi Naghiaei, Hossein A. Rahmani, Mahdi Dehghan:
The Unfairness of Popularity Bias in Book Recommendation. BIAS 2022: 69-81 - Julia Neidhardt, Mete Sertkan:
Towards an Approach for Analyzing Dynamic Aspects of Bias and Beyond-Accuracy Measures. BIAS 2022: 35-42 - Hossein A. Rahmani, Yashar Deldjoo, Ali Tourani, Mohammadmehdi Naghiaei:
The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation. BIAS 2022: 56-68 - Carlos Rojas, David Contreras, Maria Salamó:
Analysis of Biases in Calibrated Recommendations. BIAS 2022: 91-103 - Ali Shirali:
Sequential Nature of Recommender Systems Disrupts the Evaluation Process. BIAS 2022: 21-34 - 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. BIAS 2022: 117-129 - 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 [contents] - 2021
- Michael Färber, Frederic Bartscherer:
Media Bias Everywhere? A Vision for Dealing with the Manipulation of Public Opinion. BIAS 2021: 9-13
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