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[null,"1.","Record Nr.",null,"UNINA9910983492903321"]
[null,null,"Autore",null,"Bellogin Alejandro"]
[null,null,"Titolo",null,"Advances in Bias and Fairness in Information Retrieval : 5th International Workshop, BIAS 2024, Washington, DC, USA, July 18, 2024, Revised Selected Papers / / edited by Alejandro Bellogin, Ludovico Boratto, Styliani Kleanthous, Elisabeth Lex, Francesca Maridina Malloci, Mirko Marras"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"3-031-71975-1"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (113 pages)"]
[null,null,"Collana",null,"Communications in Computer and Information Science, , 1865-0937 ; ; 2227"]
[null,null,"Disciplina",null,"004.6"]
[null,null,"Soggetti",null,"Computer networks"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Electronic commerce"]
[{"$nulls":4},"Computer Communication Networks"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"e-Commerce and e-Business"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"An Offer you Cannot Refuse? Trends in the Coercive Impact of Amazon Book Recommendations -- Retention Induced Biases in a Recommendation System with Heterogeneous Users -- Political Bias of Large Language Models in Few-shot News Summarization -- Fairness Analysis of Machine Learning-Based Code Reviewer Recommendation -- Bias Reduction in Social Networks through Agent-Based Simulations -- vivaFemme: Mitigating Gender Bias in Neural Team Recommendation via Female-Advocate Loss Regularization -- Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Training."]
[null,null,null,"Sommario/riassunto","This book constitutes the refereed proceedings of the 5th International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2024, held in Washington, DC, USA, on July 18, 2024 in hybrid mode. The 7 full papers included in this book were carefully reviewed and selected from 20 submissions. They are grouped into three thematic sessions, each focusing on distinct aspects of bias and fairness in information retrieval."]