000			04163nam a22004695i 4500
988			_aSpringer_Computer_2022
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008			220618s2022 sz a s |1|| 0|eng d
020			_a9783031093166
024	7		_a10.1007/978-3-031-09316-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aZA3075 _b2022 EB
245	0	0	_aAdvances in Bias and Fairness in Information Retrieval : _bThird International Workshop, BIAS 2022, Stavanger, Norway, April 10, 2022, Revised Selected Papers _cedited by Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo
246	0		_aAdvances in Bias and Fairness in Information Retrieval : _b3rd International Workshop, BIAS 2022, Stavanger, Norway, April 10, 2022, Revised Selected Papers
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publising _c2022
300			_a1 recurso en línea (X, 155 páginas) _b35 ilustraciones, 30 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aCommunications in Computer and Information Science _x1865-0937 _v1610
505	0		_aPopularity 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.
520			_aThis 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. .
650		7	_2embne _9151819 _aAlgoritmos computacionales _vCongresos y asambleas
650		7	_2embne _9147823 _aRecuperación de la información _vCongresos y asambleas
700	1		_aBoratto, Ludovico _eeditor literario _0(orcid)0000-0002-6053-3015 _1https://orcid.org/0000-0002-6053-3015 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aFaralli, Stefano _eeditor literario _0(orcid)0000-0003-3684-8815 _1https://orcid.org/0000-0003-3684-8815 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aMarras, Mirko _eeditor literario _0(orcid)0000-0003-1989-6057 _1https://orcid.org/0000-0003-1989-6057 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aStilo, Giovanni _eeditor literario _0(orcid)0000-0002-2092-0213 _1https://orcid.org/0000-0002-2092-0213 _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783031093159
776	0	8	_iPrinted edition: _z9783031093173
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-09316-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b10/2022 _dz _esc _zSI