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Trust-based collective view prediction

Autor Tiejian Luo; Su Chen; Guandong Xu; Jia Zhou
Vydavatel: New York, NY : Springer, ©2013.
Vydání/formát:   e-kniha : Document : EnglishZobrazit všechny vydání a formáty
Databáze:WorldCat
Shrnutí:
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  Přečíst více...
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Detaily

Žánr/forma: Electronic books
Typ materiálu: Document, Internetový zdroj
Typ dokumentu: Internet Resource, Computer File
Všichni autoři/tvůrci: Tiejian Luo; Su Chen; Guandong Xu; Jia Zhou
ISBN: 9781461472025 1461472024 1461472016 9781461472018
OCLC číslo: 852689603
Popis: 1 online resource.
Obsahy: Preface --
Introduction --
Related Work --
Collaborative Filtering --
Sentiment Analysis --
Theory Foundations --
Models, Methods and Algorithms --
Framework for Robustness Analysis --
Conclusions --
Appendix.
Odpovědnost: Tiejian Luo, Su Chen, Guandong Xu, Jia Zhou.

Anotace:

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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