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Recommendation and Search in Social Networks

Overview of attention for book
Cover of 'Recommendation and Search in Social Networks'

Table of Contents

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    Book Overview
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    Chapter 1 Adaptive Identification of Hashtags for Real-Time Event Data Collection
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    Chapter 2 Comparison of Emoticon Recommendation Methods to Improve Computer-Mediated Communication
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    Chapter 3 Accuracy Versus Novelty and Diversity in Recommender Systems: A Nonuniform Random Walk Approach
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    Chapter 4 Social Network Derived Credibility
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    Chapter 5 Anonymizing Social Network Data for Maximal Frequent-Sharing Pattern Mining
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    Chapter 6 A Comprehensive Analysis of Detection of Online Paid Posters
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    Chapter 7 An Improved Collaborative Recommendation System by Integration of Social Tagging Data
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    Chapter 8 Personalization of Web Search Using Social Signals
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    Chapter 9 The Pareto Principle Is Everywhere: Finding Informative Sentences for Opinion Summarization Through Leader Detection
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    Chapter 10 Social Media Question Asking: A Developing Country Perspective
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    Chapter 11 Evolutionary Influence Maximization in Viral Marketing
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    Chapter 12 Mining and Analyzing the Italian Parliament: Party Structure and Evolution
Attention for Chapter 5: Anonymizing Social Network Data for Maximal Frequent-Sharing Pattern Mining
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Citations

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Chapter title
Anonymizing Social Network Data for Maximal Frequent-Sharing Pattern Mining
Chapter number 5
Book title
Recommendation and Search in Social Networks
Published by
Springer, Cham, January 2015
DOI 10.1007/978-3-319-14379-8_5
Book ISBNs
978-3-31-914378-1, 978-3-31-914379-8
Authors

Benjamin C. M. Fung, Yan’an Jin, Jiaming Li, Junqiang Liu, Fung, Benjamin C. M., Jin, Yan’an, Li, Jiaming, Liu, Junqiang

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 1 100%
Student > Doctoral Student 1 100%
Readers by discipline Count As %
Computer Science 2 200%

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