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

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謙虛的人

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