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Trust-based collective view prediction

Verfasser/in: Tiejian Luo; Su Chen; Guandong Xu; Jia Zhou
Verlag: New York, NY : Springer, ©2013.
Ausgabe/Format   E-Book : Dokument : EnglischAlle Ausgaben und Formate anzeigen
Datenbank:WorldCat
Zusammenfassung:
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  Weiterlesen…
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Details

Gattung/Form: Electronic books
Medientyp: Dokument, Internetquelle
Dokumenttyp: Internet-Ressource, Computer-Datei
Alle Autoren: Tiejian Luo; Su Chen; Guandong Xu; Jia Zhou
ISBN: 9781461472025 1461472024 1461472016 9781461472018
OCLC-Nummer: 852689603
Beschreibung: 1 online resource.
Inhalt: Preface --
Introduction --
Related Work --
Collaborative Filtering --
Sentiment Analysis --
Theory Foundations --
Models, Methods and Algorithms --
Framework for Robustness Analysis --
Conclusions --
Appendix.
Verfasserangabe: [by] Tiejian Luo, Su Chen, Guandong Xu, Jia Zhou.

Abstract:

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.

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