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Title Trust-based collective view prediction / Tiejian Luo, Su Chen, Guandong Xu, Jia Zhou
Published New York, NY : Springer, [2013]
Online access available from:
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Description 1 online resource
Contents Preface -- Introduction -- Related Work -- Collaborative Filtering -- Sentiment Analysis -- Theory Foundations -- Models, Methods and Algorithms -- Framework for Robustness Analysis -- Conclusions -- Appendix
Summary Collective view prediction is to judge the opinions of an active web user based on unknown elements by referring to the collective mind of the whole community. Content-based recommendation and collaborative filtering are two mainstream collective view prediction techniques. They generate predictions by analyzing the text features of the target object or the similarity of users' past behaviors. Still, these techniques are vulnerable to the artificially-injected noise data, because they are not able to judge the reliability and credibility of the information sources. Trust-based Collective View Prediction describes new approaches for tackling this problem by utilizing users' trust relationships from the perspectives of fundamental theory, trust-based collective view prediction algorithms and real case studies
Analysis Computer science
Data mining
Artificial intelligence
Information Systems Applications (incl. Internet)
Bibliography Includes bibliographical references and index
Notes English
Online resource; title from PDF title page (SpringerLink, viewed July 12, 2013)
Subject Computer logic.
Data mining.
Data Mining.
Form Electronic book
Author Luo, Tiejian, author
Chen, Su, author
Xu, Guandong, author
Zhou, Jia, author
LC no. 2013934720
ISBN 9781461472025 (electronic bk.)
1461472024 (electronic bk.)