Social Information Access: Systems and Technologies
Author:   Peter Brusilovsky ,  Daqing He
Publisher:   Springer International Publishing AG
Edition:   1st ed. 2018
Volume:   10100
ISBN:  

9783319900919


Pages:   655
Publication Date:   03 May 2018
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Social Information Access: Systems and Technologies


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Overview

Social information access is defined as a stream of research that explores methods for organizing the past interactions of users in a community in order to provide future users with better access to information. Social information access covers a wide range of different technologies and strategies that operate on a different scale, which can range from a small closed corpus site to the whole Web. The 16 chapters included in this book provide a broad overview of modern research on social information access. In order to provide a balanced coverage, these chapters are organized by the main types of information access (i.e., social search, social navigation, and recommendation) and main sources of social information.  

Full Product Details

Author:   Peter Brusilovsky ,  Daqing He
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   1st ed. 2018
Volume:   10100
Weight:   1.003kg
ISBN:  

9783319900919


ISBN 10:   3319900919
Pages:   655
Publication Date:   03 May 2018
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Social Information Access: Definition and Classification.- Privacy in Social Information Access.- Social Q&A.- Collaborative Information Search.- Social Navigation.- Tag-Based Navigation and Visualization.- Social Search.- Network-Based Social Search.- Accessing Information with Tags: Search and Ranking.- Rating-Based Collaborative Filtering: Algorithms and Evaluation.- Recommendations Based on Social Links.- Tag-Based Recommendation.- From Opinions to Recommendations.- Recommending Based on Implicit Feedback.- People Recommendation on Social Media.- Location Recommendation with Social Media Data. 

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Author Information

Peter Brusilovsky, University of Pittsburgh, School of Computing and Information, Pittsburgh, PA, USA

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