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Advances in Bias and Fairness in Information Retrieval [[electronic resource] ] : 4th International Workshop, BIAS 2023, Dublin, Ireland, April 2, 2023, Revised Selected Papers / / edited by Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo
Advances in Bias and Fairness in Information Retrieval [[electronic resource] ] : 4th International Workshop, BIAS 2023, Dublin, Ireland, April 2, 2023, Revised Selected Papers / / edited by Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo
Autore Boratto Ludovico
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (187 pages)
Disciplina 025.524
Altri autori (Persone) FaralliStefano
MarrasMirko
StiloGiovanni
Collana Communications in Computer and Information Science
Soggetto topico Computer engineering
Computer networks
Artificial intelligence
Electronic commerce
Computer Engineering and Networks
Artificial Intelligence
e-Commerce and e-Business
ISBN 3-031-37249-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations -- Measuring Bias in Multimodal Models: Multimodal Composite Association Score -- Evaluating Fairness Metrics -- Utilizing Implicit Feedback for User Mainstreaminess Evaluation and Bias Detection in Recommender Systems -- Preserving Utility in Fair Top-k Ranking with Intersectional Bias -- Mitigating Position Bias in Hotels Recommender Systems -- Improving Recommender System Diversity with Variational Autoencoders -- Addressing Biases in the Texts using an End-to-End Pipeline Approach -- Bootless Application of Greedy Re-ranking Algorithms in Fair Neural Team Formation -- How do you feel? Information Retrieval in Psychotherapy and Fair Ranking Assessment -- Understanding Search Behavior Bias in Wikipedia -- Do you MIND? Reflections on the MIND dataset for research on diversity in news recommendations -- Detecting and Measuring Social Bias of Arabic Generative Models in the Context of Search and Recommendation -- What are we missing in algorithmic fairness? Discussing open challenges for fairness analysis in user profiling with Graph Neural Networks.
Record Nr. UNISA-996546825403316
Boratto Ludovico  
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Advances in Bias and Fairness in Information Retrieval : 4th International Workshop, BIAS 2023, Dublin, Ireland, April 2, 2023, Revised Selected Papers / / edited by 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 / / edited by Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo
Autore Boratto Ludovico
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (187 pages)
Disciplina 025.524
Altri autori (Persone) FaralliStefano
MarrasMirko
StiloGiovanni
Collana Communications in Computer and Information Science
Soggetto topico Computer engineering
Computer networks
Artificial intelligence
Electronic commerce
Computer Engineering and Networks
Artificial Intelligence
e-Commerce and e-Business
ISBN 3-031-37249-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations -- Measuring Bias in Multimodal Models: Multimodal Composite Association Score -- Evaluating Fairness Metrics -- Utilizing Implicit Feedback for User Mainstreaminess Evaluation and Bias Detection in Recommender Systems -- Preserving Utility in Fair Top-k Ranking with Intersectional Bias -- Mitigating Position Bias in Hotels Recommender Systems -- Improving Recommender System Diversity with Variational Autoencoders -- Addressing Biases in the Texts using an End-to-End Pipeline Approach -- Bootless Application of Greedy Re-ranking Algorithms in Fair Neural Team Formation -- How do you feel? Information Retrieval in Psychotherapy and Fair Ranking Assessment -- Understanding Search Behavior Bias in Wikipedia -- Do you MIND? Reflections on the MIND dataset for research on diversity in news recommendations -- Detecting and Measuring Social Bias of Arabic Generative Models in the Context of Search and Recommendation -- What are we missing in algorithmic fairness? Discussing open challenges for fairness analysis in user profiling with Graph Neural Networks.
Record Nr. UNINA-9910734876903321
Boratto Ludovico  
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui