Bias and social aspects in search and recommendation : first International Workshop, BIAS 2020, Lisbon, Portugal, April 14, Proceedings / Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo (eds.).
BIAS (Workshop) (1st : 2020 : Online); Boratto, Ludovico.; Faralli, Stefano.; Marras, Mirko.; Stilo, Giovanni.
2020
ZA3084
Available Online

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Title Bias and social aspects in search and recommendation : first International Workshop, BIAS 2020, Lisbon, Portugal, April 14, Proceedings / Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo (eds.).
Meeting Name BIAS (Workshop) (1st : 2020 : Online)
ISBN 9783030524852 (electronic book)
303052485X (electronic book)
9783030524845
DOI https://doi.org/10.1007/978-3-030-52485-2
Publication Details Cham : Springer, 2020.
Language English
Description 1 online resource (216 pages).
Item Number 10.1007/978-3-030-52485-2 doi
10.1007/978-3-030-52
Call Number ZA3084
Dewey Decimal Classification 005.5/6
Summary 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.
Note International conference proceedings.
"Due to the COVID-19 pandemic BIAS 2020 was held virtually."
Includes author index.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Boratto, Ludovico.
Faralli, Stefano.
Marras, Mirko.
Stilo, Giovanni.
Series Communications in computer and information science ; 1245.
Available in Other Form Bias and Social Aspects in Search and Recommendation : First International Workshop, BIAS 2020, Lisbon, Portugal, April 14, Proceedings
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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.

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