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Trust-based collective view prediction

Autor: Tiejian Luo; Su Chen; Guandong Xu; Jia Zhou
Editorial: New York, NY : Springer, ©2013.
Edición/Formato:   Libro-e : Documento : Inglés (eng)Ver todas las ediciones y todos los formatos
Base de datos:WorldCat
Resumen:
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  Leer más
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Detalles

Género/Forma: Electronic books
Ebook
Tipo de material: Documento, Recurso en Internet
Tipo de documento Recurso internet, Archivo de computadora
Todos autores / colaboradores: Tiejian Luo; Su Chen; Guandong Xu; Jia Zhou
ISBN: 9781461472025 1461472024 1461472016 9781461472018
Número OCLC: 852689603
Descripción: 1 online resource
Contenido: Preface --
Introduction --
Related Work --
Collaborative Filtering --
Sentiment Analysis --
Theory Foundations --
Models, Methods and Algorithms --
Framework for Robustness Analysis --
Conclusions --
Appendix.
Responsabilidad: Tiejian Luo, Su Chen, Guandong Xu, Jia Zhou.

Resumen:

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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