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Trust-based collective view prediction

저자: Tiejian Luo; et al
출판사: New York, NY : Springer, ©2013.
판/형식:   전자도서 : 문서 : 영어모든 판과 형식 보기
데이터베이스:WorldCat
요약:
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  더 읽기…
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장르/형태: Electronic books
자료 유형: 문서, 인터넷 자료
문서 형식: 인터넷 자원, 컴퓨터 파일
모든 저자 / 참여자: Tiejian Luo; et al
ISBN: 9781461472025 1461472024
OCLC 번호: 852689603
설명: 1 online resource.
내용: Preface --
Introduction --
Related Work --
Collaborative Filtering --
Sentiment Analysis --
Theory Foundations --
Models, Methods and Algorithms --
Framework for Robustness Analysis --
Conclusions --
Appendix.
책임: Tiejian Luo...[et al.].
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초록:

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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