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Titlebook: Bias and Social Aspects in Search and Recommendation; First International Ludovico Boratto,Stefano Faralli,Giovanni Stilo Conference proce

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期刊全称Bias and Social Aspects in Search and Recommendation
期刊简称First International
影响因子2023Ludovico Boratto,Stefano Faralli,Giovanni Stilo
视频video
学科分类Communications in Computer and Information Science
图书封面Titlebook: Bias and Social Aspects in Search and Recommendation; First International  Ludovico Boratto,Stefano Faralli,Giovanni Stilo Conference proce
影响因子This book constitutes refereed proceedings of the First International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2020, held in April, 2020. Due to the COVID-19 pandemic BIAS 2020 was held virtually. .The 10 full papers and 7 short papers were carefully reviewed and seleced from 44 submissions. The papers cover topics that go from search and recommendation in online dating, education, and social media, over the impact ofgender bias in word embeddings, to tools that allow to explore bias and fairnesson the Web. .
Pindex Conference proceedings 2020
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Mitigating Gender Bias in Machine Learning Data Sets,rithms has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to address such issues have involved testing learned associations, integrating concepts of fairness to machine le
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: Exploring the Bias of Web Domains Through the Eyes of Users,em, that based on the web graph computes the bias characteristics of web domains to user-defined concepts. Our approach uses adaptations of propagation models and a variation of the pagerank algorithm named ., that models various behaviours of biased surfers. Currently, the system runs over a subset
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A Novel Similarity Measure for Group Recommender Systems with Optimal Time Complexity,wever, a unique user profile is created, containing all our preferences. Suppose that a company wants to understand who are its customers. It wants to treat costumers as a target, and understand what campaigns the company should run on them. On the one hand, an approach that clusters the users and p
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