LEADER 04109nam 22007095 450 001 9910578689203321 005 20251225203516.0 010 $a3-031-09316-X 024 7 $a10.1007/978-3-031-09316-6 035 $a(MiAaPQ)EBC7019576 035 $a(Au-PeEL)EBL7019576 035 $a(CKB)23976614800041 035 $aEBL7019576 035 $a(AU-PeEL)EBL7019576 035 $a(PPN)269152016 035 $a(BIP)84610185 035 $a(BIP)84294624 035 $a(DE-He213)978-3-031-09316-6 035 $a(EXLCZ)9923976614800041 100 $a20220618d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Bias and Fairness in Information Retrieval $eThird International Workshop, BIAS 2022, Stavanger, Norway, April 10, 2022, Revised Selected Papers /$fedited by Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (166 pages) 225 1 $aCommunications in Computer and Information Science,$x1865-0937 ;$v1610 300 $aDescription based upon print version of record. 311 08$aPrint version: Boratto, Ludovico Advances in Bias and Fairness in Information Retrieval Cham : Springer International Publishing AG,c2022 9783031093159 320 $aIncludes bibliographical references and index. 327 $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. 330 $aThis book constitutes refereed proceedings of the Third International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2022, held in April, 2022. The 9 full papers and 4 short papers were carefully reviewed and selected from 34 submissions. The papers cover topics that go from search and recommendation in online dating, education, and social media, over the impact of gender bias in word embeddings, to tools that allow to explore bias and fairnesson the Web. . 410 0$aCommunications in Computer and Information Science,$x1865-0937 ;$v1610 606 $aComputer engineering 606 $aComputer networks 606 $aArtificial intelligence 606 $aElectronic commerce 606 $aComputer Engineering and Networks 606 $aArtificial Intelligence 606 $ae-Commerce and e-Business 606 $aComputer Engineering and Networks 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aArtificial intelligence. 615 0$aElectronic commerce. 615 14$aComputer Engineering and Networks. 615 24$aArtificial Intelligence. 615 24$ae-Commerce and e-Business. 615 24$aComputer Engineering and Networks. 676 $a025.524 676 $a025.524 702 $aBoratto$b Ludovico 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910578689203321 996 $aAdvances in Bias and Fairness in Information Retrieval$92883274 997 $aUNINA