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ISBN: 9781461468943
[ED: Taschenbuch], [PU: Springer-Verlag GmbH], Neuware - Recommender systems are one of the recent inventions to deal with the ever-growing information overload in relation to the selecti… Más…
2013, ISBN: 1461468949
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. Collaborati… Más…
Trust for Intelligent Recommendation (SpringerBriefs in Electrical and Computer Engineering) - Pasta blanda
2013
ISBN: 9781461468943
Springer, Taschenbuch, Auflage: 2013, 136 Seiten, Publiziert: 2013-03-30T00:00:01Z, Produktgruppe: Buch, Hersteller-Nr.: 37071175, 0.45 kg, Datenbanken, Computer & Internet, Kategorien, B… Más…
2013, ISBN: 1461468949
2013 Kartoniert / Broschiert Data Mining (EDV), Informationssystem, Intelligenz / Künstliche Intelligenz, KI, Künstliche Intelligenz - AI, Data Mining, Angewandte Informatik, Künstliche… Más…
2013, ISBN: 1461468949
[EAN: 9781461468943], Neubuch, [PU: Springer], PRINT ON DEMAND Book; New; Fast Shipping from the UK., Books
Datos bibliográficos del mejor libro coincidente
Detalles del libro - Trust for Intelligent Recommendation (SpringerBriefs in Electrical and Computer Engineering)
EAN (ISBN-13): 9781461468943
ISBN (ISBN-10): 1461468949
Tapa dura
Tapa blanda
Año de publicación: 2013
Editorial: Springer
119 Páginas
Peso: 0,224 kg
Idioma: Englisch
Libro en la base de datos desde 2008-04-13T03:18:49-05:00 (Mexico City)
Página de detalles modificada por última vez el 2023-10-14T03:36:46-06:00 (Mexico City)
ISBN/EAN: 1461468949
ISBN - escritura alterna:
1-4614-6894-9, 978-1-4614-6894-3
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Título del libro: intelligent, god trust
Datos del la editorial
Autor: Touhid Bhuiyan
Título: SpringerBriefs in Electrical and Computer Engineering; Trust for Intelligent Recommendation
Editorial: Springer; Springer US
119 Páginas
Año de publicación: 2013-03-30
New York; NY; US
Impreso en
Idioma: Inglés
42,79 € (DE)
43,99 € (AT)
53,60 CHF (CH)
POD
XIV, 119 p. 34 illus.
BC; Hardcover, Softcover / Informatik, EDV/Informatik; Data Mining; Verstehen; Collaborative Filtering; Inference; Recommender Systems; Trust; social networks; Data Mining and Knowledge Discovery; Computer and Information Systems Applications; Artificial Intelligence; Wissensbasierte Systeme, Expertensysteme; Angewandte Informatik; Künstliche Intelligenz; EA
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 bookvaluable.
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.
Includes supplementary material: sn.pub/extras
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