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Trust-based collective view prediction

作者: Tiejian Luo; et al
出版商: New York, NY : Springer, ©2013.
版本/格式:   電子書 : 文獻 : 英語所有版本和格式的總覽
資料庫:WorldCat
提要:
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  再讀一些...
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類型/形式: Electronic books
資料類型: 文獻, 網際網路資源
文件類型: 網路資源, 電腦資料
所有的作者/貢獻者: Tiejian Luo; et al
ISBN: 9781461472025 1461472024
OCLC系統控制編碼: 852689603
描述: 1 online resource.
内容: Preface --
Introduction --
Related Work --
Collaborative Filtering --
Sentiment Analysis --
Theory Foundations --
Models, Methods and Algorithms --
Framework for Robustness Analysis --
Conclusions --
Appendix.
責任: Tiejian Luo...[et al.].
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摘要:

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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