OPAC

検索結果一覧に戻る

Advances in Bias and Fairness in Information Retrieval : Third International Workshop, BIAS 2022, Stavanger, Norway, April 10, 2022, Revised Selected Papers / edited by Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo
(Communications in Computer and Information Science. ISSN:18650937 ; 1610)

データ種別	電子ブック
版	1st ed. 2022.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2022
大きさ	X, 155 p. 35 illus., 30 illus. in color : online resource
著者標目	Boratto, Ludovico editor Faralli, Stefano editor Marras, Mirko editor Stilo, Giovanni editor SpringerLink (Online service)

所蔵情報を非表示

URL	巻　次	配架場所	請求記号	登録番号	コメント	刷　年	状　態	利用注記	ISBN	予約	請求メモ
URL		射水-電子	007	EB0002162	Computer Scinece R0 2005-6,2022-3				9783031093166

書誌詳細を非表示

一般注記	Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems -- Recommender Systems and Users' Behaviour Effect on Choice's Distribution and Quality -- Sequential Nature of Recommender Systems Disrupts the Evaluation Process -- Towards an Approach for Analyzing Dynamic Aspects of Bias and Beyond-Accuracy Measures -- A Crowdsourcing Methodology to Measure Algorithmic Bias in Black-box Systems: A Case Study with COVID-related Searches -- The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation -- The Unfairness of Popularity Bias in Book Recommendation -- Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches -- Analysis of Biases in Calibrated Recommendations -- Do Perceived Gender Biases in Retrieval Results affect Users’ Relevance Judgements? -- Enhancing Fairness in Classification Tasks with Multiple Variables: a Data- and Model-Agnostic Approach -- Keyword Recommendation for Fair Search -- FARGO: a Fair, context-AwaRe, Group recOmmender system 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. HTTP:URL=https://doi.org/10.1007/978-3-031-09316-6
件　名	LCSH:Computer engineering LCSH:Computer networks LCSH:Artificial intelligence LCSH:Electronic commerce FREE:Computer Engineering and Networks FREE:Artificial Intelligence FREE:e-Commerce and e-Business FREE:Computer Engineering and Networks
分　類	LCC:TK7885-7895 LCC:TK5105.5-5105.9 LCC:TK7885-7895 LCC:TK5105.5-5105.9 DC23:621.39 DC23:004.6 DC23:621.39 DC23:004.6
書誌ID	EB00001550
ISBN	9783031093166