Bias and Social Aspects in Search and Recommendation

First International Workshop, BIAS 2020, Lisbon, Portugal, April 14, Proceedings
1. Auflage 2020
ISBN: 978-3-030-52484-5
Verlag: Springer International Publishing

Buch, Englisch, Band 1245, 205 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 341 g

Reihe: Communications in Computer and Information Science

This 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.

Zielgruppe

Research

Autoren/Hrsg.

Boratto, Ludovico

Stilo, Giovanni

Faralli, Stefano

Marras, Mirko

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Inhaltsverzeichnis

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.