LEADER 04328nam 22007455 450 001 9910413447003321 005 20230810171301.0 010 $a3-030-52485-X 024 7 $a10.1007/978-3-030-52485-2 035 $a(CKB)4100000011343278 035 $a(DE-He213)978-3-030-52485-2 035 $a(MiAaPQ)EBC6272315 035 $a(PPN)250217988 035 $a(EXLCZ)994100000011343278 100 $a20200711d2020 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBias and Social Aspects in Search and Recommendation $eFirst International Workshop, BIAS 2020, Lisbon, Portugal, April 14, Proceedings /$fedited by Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource $cillustrations (chiefly color) 225 1 $aCommunications in Computer and Information Science,$x1865-0937 ;$v1245 311 $a3-030-52484-1 320 $aIncludes bibliographical references and index. 327 $aFacets of Fairness in Search and Recommendation -- Mitigating Gender Bias in Machine Learning Data Sets -- Why Do We Need To Be Bots? What Prevents Society From Detecting Biases in Recommendation Systems -- Effect of Debiasing on Information Retrieval -- Matchmaking Under Fairness Constraints: a Speed Dating Case Study -- Recommendation Filtering à la Carte for Intelligent Tutoring Systems -- Bias Goggles - Exploring the bias of Web Domains through the Eyes of the Users -- Data Pipelines for Personalized Exploration of Rated Datasets -- Beyond Accuracy in Link Prediction -- A Novel Similarity Measure for Group Recommender Systems with Optimal Time Complexity -- What Kind of Content are you Prone to Tweet? Multi-topic Preference Model for Tweeters -- Venue Suggestion Using Social-Centric Scores -- The Impact of Foursquare Checkins on Users? Emotions on Twitter -- Improving News Personalization through Search Logs -- Analyzing the Interaction of Users with News Articles to Create Personalization Services -- Using String-Comparison measures to Improve and Evaluate Collaborative Filtering Recommender Systems -- Enriching Product Catalogs with User Opinions. 330 $aThis book constitutes refereed proceedings of the First International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2020, held in April, 2020. Due to the COVID-19 pandemic BIAS 2020 was held virtually. The 10 full papers and 7 short papers were carefully reviewed and seleced from 44 submissions. The papers cover topics that go from search and recommendation in online dating, education, and social media, over the impact ofgender bias in word embeddings, to tools that allow to explore bias and fairnesson the Web. . 410 0$aCommunications in Computer and Information Science,$x1865-0937 ;$v1245 606 $aDatabase management 606 $aArtificial intelligence 606 $aComputer engineering 606 $aComputer networks 606 $aSocial sciences$xData processing 606 $aElectronic commerce 606 $aDatabase Management System 606 $aArtificial Intelligence 606 $aComputer Engineering and Networks 606 $aComputer Application in Social and Behavioral Sciences 606 $ae-Commerce and e-Business 615 0$aDatabase management. 615 0$aArtificial intelligence. 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aSocial sciences$xData processing. 615 0$aElectronic commerce. 615 14$aDatabase Management System. 615 24$aArtificial Intelligence. 615 24$aComputer Engineering and Networks. 615 24$aComputer Application in Social and Behavioral Sciences. 615 24$ae-Commerce and e-Business. 676 $a005.56 676 $a005.56 702 $aBoratto$b Ludovico 702 $aFaralli$b Stefano 702 $aMarras$b Mirko 702 $aStilo$b Giovanni 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910413447003321 996 $aBias and social aspects in search and recommendation$92045983 997 $aUNINA