LEADER 03545nam 22005295 450 001 996465363503316 005 20210302191658.0 010 $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 uy 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 $efirst International Workshop, BIAS 2020, Lisbon, Portugal, April 14, Proceedings /$fLudovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo (eds.) 210 1$aCham :$cSpringer,$d[2020] 215 $a1 online resource $cillustrations (chiefly color) 225 1 $aCommunications in Computer and Information Science,$x1865-0929 ;$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 ;$v1245. 606 $aRecommender systems (Information filtering)$vCongresses 606 $aInformation retrieval$vCongresses 606 $aDiscrimination$vCongresses 615 0$aRecommender systems (Information filtering) 615 0$aInformation retrieval 615 0$aDiscrimination 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 $a996465363503316 996 $aBias and social aspects in search and recommendation$92045983 997 $aUNISA