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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
|
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
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 Aprenentatge automàtic Xarxes neuronals (Informàtica) Intel·ligència artificial Algorismes |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN |
9783031372490
3031372492 |
| 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 | ||
| ||