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Titlebook: Advances in Bias and Fairness in Information Retrieval; Third International Ludovico Boratto,Stefano Faralli,Giovanni Stilo Conference pro

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發(fā)表于 2025-3-21 19:15:03 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Advances in Bias and Fairness in Information Retrieval
期刊簡(jiǎn)稱Third International
影響因子2023Ludovico Boratto,Stefano Faralli,Giovanni Stilo
視頻videohttp://file.papertrans.cn/147/146850/146850.mp4
學(xué)科分類(lèi)Communications in Computer and Information Science
圖書(shū)封面Titlebook: Advances in Bias and Fairness in Information Retrieval; Third International  Ludovico Boratto,Stefano Faralli,Giovanni Stilo Conference pro
影響因子This book constitutes refereed proceedings of the?Third International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2022, held in April, 2022.?.The 9 full papers and 4 short papers were carefully reviewed and selected from 34 submissions.?The papers cover topics that go from search and recommendation in online dating, education, and social media, over the impact of gender bias in word embeddings, to tools that allow to explore bias and fairnesson the Web.?.
Pindex Conference proceedings 2022
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Teen TV Translations: Across the Pond quality of the choices. By simulating users’ choices, influenced by RSs, it was shown that algorithmic biases, such as the tendency to recommend popular items, are transferred to the users’ choices..In this paper we conjecture that the effect of an RS on the quality and distribution of the users’ c
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The Professional Career of A. G. Dennistoni.e., frequently rated items) are recommended frequently while less popular items are recommended rarely or not at all. Researchers adopted two approaches to examining popularity bias: (i) from the users’ perspective, by analyzing how far a recommendation system deviates from user’s expectations in
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發(fā)表于 2025-3-22 21:18:48 | 只看該作者
Intelligence and Strategy in World War IIostly promoting only already popular items. Various approaches of quantifying and mitigating such biases were put forward in the literature. Most recently, . methods were proposed that aim to match the popularity of the recommended items with popularity preferences of individual users. In this paper
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K. G. Robertson (Lecturer in Sociology)the most prominent is ., which analyzes if existing imbalances in the input data are exacerbated in the produced recommendations. On the other hand, . ensure that the recommendations reflect the distribution of the original preferences of each user (e.g., in terms of item genres). In this paper, we
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K. G. Robertson (Lecturer in Sociology)n particular, since relevance in information retrieval (IR) is a multi-dimensional concept, we study whether the value and quality of the retrieved documents for some . queries can be judged differently when the content of the documents represents different genders. To this aim, we conduct a set of
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