Bias and Social Aspects in Search and Recommendation

First International Workshop, BIAS 2020, Lisbon, Portugal, April 14, Proceedings

Häftad, Engelska, 2020

AvLudovico Boratto,Stefano Faralli,Mirko Marras,Giovanni Stilo

689 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.

This book constitutes refereed proceedings of the First International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2020, held in April, 2020.

Produktinformation

Utgivningsdatum2020-07-12
Mått155 x 235 x 13 mm
Vikt341 g
FormatHäftad
SpråkEngelska
SerieCommunications in Computer and Information Science
Antal sidor205
FörlagSpringer Nature Switzerland AG
ISBN9783030524845

Tillhör följande kategorier

Tillämpad datateknik inom Data och it
Programmeringsböcker inom Data och it
E-handel inom Ekonomi och ledarskap
Artificiell intelligens inom Data och it
Nätverk och kommunikation inom Data och it
Affärsapplikationer inom Data och it
Hårdvara inom Data och it
Databaser inom Data och it

Facets 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.