Trust-based Collective View Prediction (Record no. 44833)
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fixed length control field | 03904nam a22005057a 4500 |
001 - CONTROL NUMBER | |
control field | sulb-eb0022741 |
003 - CONTROL NUMBER IDENTIFIER | |
control field | BD-SySUS |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20160413122315.0 |
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 |
Subject category code | UYQE |
Source | bicssc |
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 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
Place of production, publication, distribution, manufacture | New York, NY : |
Name of producer, publisher, distributor, manufacturer | Springer New York : |
-- | Imprint: Springer, |
Date of production, publication, distribution, manufacture, or copyright notice | 2013. |
300 ## - PHYSICAL DESCRIPTION | |
Extent | XI, 146 p. |
Other physical details | online resource. |
336 ## - CONTENT TYPE | |
Content type term | text |
Content type code | txt |
Source | rdacontent |
337 ## - MEDIA TYPE | |
Media type term | computer |
Media type code | c |
Source | rdamedia |
338 ## - CARRIER TYPE | |
Carrier type term | online resource |
Carrier type code | cr |
Source | rdacarrier |
347 ## - DIGITAL FILE CHARACTERISTICS | |
File type | text file |
Encoding format | |
Source | 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. |
Topical term or geographic name as entry element | Data mining. |
Topical term or geographic name as entry element | Artificial intelligence. |
Topical term or geographic name as entry element | Computer Science. |
Topical term or geographic name as entry element | Data Mining and Knowledge Discovery. |
Topical term or geographic name as entry element | Information Systems Applications (incl. Internet). |
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. |
Personal name | Xu, Guandong. |
Relator term | author. |
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 | |
Relationship information | Printed edition: |
International Standard Book Number | 9781461472018 |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | <a href="http://dx.doi.org/10.1007/978-1-4614-7202-5">http://dx.doi.org/10.1007/978-1-4614-7202-5</a> |
912 ## - | |
-- | ZDB-2-SCS |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Source of classification or shelving scheme | |
Koha item type |
No items available.