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Bias and Social Aspects in Search and Recommendation

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Cover of 'Bias and Social Aspects in Search and Recommendation'

Table of Contents

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    Book Overview
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    Chapter 1 Facets of Fairness in Search and Recommendation
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    Chapter 2 Mitigating Gender Bias in Machine Learning Data Sets
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    Chapter 3 Why Do We Need to Be Bots? What Prevents Society from Detecting Biases in Recommendation Systems
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    Chapter 4 Effect of Debiasing on Information Retrieval
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    Chapter 5 Matchmaking Under Fairness Constraints: A Speed Dating Case Study
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    Chapter 6 Recommendation Filtering à la carte for Intelligent Tutoring Systems
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    Chapter 7 : Exploring the Bias of Web Domains Through the Eyes of Users
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    Chapter 8 Data Pipelines for Personalized Exploration of Rated Datasets
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    Chapter 9 Beyond Accuracy in Link Prediction
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    Chapter 10 A Novel Similarity Measure for Group Recommender Systems with Optimal Time Complexity
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    Chapter 11 What Kind of Content Are You Prone to Tweet? Multi-topic Preference Model for Tweeters
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    Chapter 12 Venue Suggestion Using Social-Centric Scores
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    Chapter 13 The Impact of Foursquare Checkins on Users’ Emotions on Twitter
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    Chapter 14 Improving News Personalization Through Search Logs
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    Chapter 15 Analyzing the Interaction of Users with News Articles to Create Personalization Services
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    Chapter 16 Using String-Comparison Measures to Improve and Evaluate Collaborative Filtering Recommender Systems
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    Chapter 17 Enriching Product Catalogs with User Opinions
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Title
Bias and Social Aspects in Search and Recommendation
Published by
Springer International Publishing, September 2020
DOI 10.1007/978-3-030-52485-2
ISBNs
978-3-03-052484-5, 978-3-03-052485-2
Editors

Boratto, Ludovico, Faralli, Stefano, Marras, Mirko, Stilo, Giovanni

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