Trust-based Collective View Prediction (Record no. 95870)

000 -LEADER
fixed length control field 03864nam a22004935i 4500
001 - CONTROL NUMBER
control field 978-1-4614-7202-5
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20140220082828.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 130704s2013 xxu| s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781461472025
-- 978-1-4614-7202-5
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-1-4614-7202-5
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA76.9.D343
072 #7 - SUBJECT CATEGORY CODE
Subject category code UNF
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQE
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM021030
Source bisacsh
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.312
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Luo, Tiejian.
Relator term author.
245 10 - TITLE STATEMENT
Title Trust-based Collective View Prediction
Medium [electronic resource] /
Statement of responsibility, etc by Tiejian Luo, Su Chen, Guandong Xu, Jia Zhou.
264 #1 -
-- New York, NY :
-- Springer New York :
-- Imprint: Springer,
-- 2013.
300 ## - PHYSICAL DESCRIPTION
Extent XI, 146 p. 41 illus.
Other physical details online resource.
336 ## -
-- text
-- txt
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-- computer
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-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Preface -- Introduction -- Related Work -- Collaborative Filtering -- Sentiment Analysis -- Theory Foundations -- Models, Methods and Algorithms -- Framework for Robustness Analysis -- Conclusions -- Appendix.
520 ## - SUMMARY, ETC.
Summary, etc 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. The book consists of two main parts – a theoretical foundation and an algorithmic study. The first part will review several basic concepts and methods related to collective view prediction, such as state-of-the-art recommender systems, sentimental analysis, collective view, trust management, the Relationship of Collective View and Trustworthy, and trust in collective view prediction. In the second part, the authors present their models and algorithms based on a quantitative analysis of more than 300 thousand users’ data from popular product-reviewing websites. They also introduce two new trust-based prediction algorithms, one collaborative algorithm based on the second-order Markov random walk model, and one Bayesian fitting model for combining multiple predictors. The discussed concepts, developed algorithms, empirical results, evaluation methodologies and the robust analysis framework described in Trust-based Collective View Prediction will not only provide valuable insights and findings to related research communities and peers, but also showcase the great potential to encourage industries and business partners to integrate these techniques into new applications.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial intelligence.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer Science.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data Mining and Knowledge Discovery.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Information Systems Applications (incl. Internet).
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial Intelligence (incl. Robotics).
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Chen, Su.
Relator term author.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Xu, Guandong.
Relator term author.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Zhou, Jia.
Relator term author.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9781461472018
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-1-4614-7202-5
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