Trust for Intelligent Recommendation (Record no. 54609)

000 -LEADER
fixed length control field 04049nam a22004935i 4500
001 - CONTROL NUMBER
control field 978-1-4614-6895-0
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200421111655.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 130331s2013 xxu| s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781461468950
-- 978-1-4614-6895-0
082 04 - CLASSIFICATION NUMBER
Call Number 006.312
100 1# - AUTHOR NAME
Author Bhuiyan, Touhid.
245 10 - TITLE STATEMENT
Title Trust for Intelligent Recommendation
300 ## - PHYSICAL DESCRIPTION
Number of Pages XIV, 119 p. 34 illus.
490 1# - SERIES STATEMENT
Series statement SpringerBriefs in Electrical and Computer Engineering,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Introduction -- Literature Review -- Trust Inferences using Subjective Logic -- Online Survey on Trust and Interest Similarity -- SimTrust: The Algorithm for Similarity-based Trust Network Generation -- Experiments and Evaluation -- Conclusions -- Appendix A: Sample Survey Questions -- Appendix B:  Glossary of Terms and Abbreviations -- Appendix C:  Combining Trust & Reputation Management.
520 ## - SUMMARY, ETC.
Summary, etc Recommender systems are one of the recent inventions to deal with the ever-growing information overload in relation to the selection of goods and services in a global economy. Collaborative Filtering (CF) is one of the most popular techniques in recommender systems. The CF recommends items to a target user based on the preferences of a set of similar users known as the neighbors, generated from a database made up of the preferences of past users. In the absence of these ratings, trust between the users could be used to choose the neighbor for recommendation making. Better recommendations can be achieved using an inferred trust network which mimics the real world "friend of a friend" recommendations. To extend the boundaries of the neighbor, an effective trust inference technique is required. This book proposes a trust interference technique called Directed Series Parallel Graph (DSPG) that has empirically outperformed other popular trust inference algorithms, such as TidalTrust and MoleTrust. For times when reliable explicit trust data is not available, this book outlines a new method called SimTrust for developing trust networks based on a user's interest similarity. To identify the interest similarity, a user's personalized tagging information is used. However, particular emphasis is given in what resources the user chooses to tag, rather than the text of the tag applied. The commonalities of the resources being tagged by the users can be used to form the neighbors used in the automated recommender system. Through a series of case studies and empirical results, this book highlights the effectiveness of this tag-similarity based method over the traditional collaborative filtering approach, which typically uses rating data. Trust for Intelligent Recommendation is intended for practitioners as a reference guide for developing improved, trust-based recommender systems. Researchers in a related field will also find this book valuable.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-1-4614-6895-0
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- New York, NY :
-- Springer New York :
-- Imprint: Springer,
-- 2013.
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-- computer
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-- rdamedia
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-- online resource
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-- text file
-- PDF
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer science.
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-- Data mining.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer Science.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data Mining and Knowledge Discovery.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Information Systems Applications (incl. Internet).
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence (incl. Robotics).
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2191-8112
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-- ZDB-2-SCS

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