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Published by Springer New York, 2013
ISBN 10: 1461472016ISBN 13: 9781461472018
Seller: Buchpark, Trebbin, Germany
Book
Condition: Wie neu. Zustand: Wie neu | Seiten: 160.
Published by Springer, 2013
ISBN 10: 1461472016ISBN 13: 9781461472018
Seller: booksXpress, Bayonne, NJ, U.S.A.
Book
Hardcover. Condition: new.
Published by Springer, 2015
ISBN 10: 1489992006ISBN 13: 9781489992000
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
Book
Condition: New.
Published by Springer, 2013
ISBN 10: 1461472016ISBN 13: 9781461472018
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
Book
Condition: New.
Published by Springer, 2015
ISBN 10: 1489992006ISBN 13: 9781489992000
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Book Print on Demand
Condition: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book.
Published by Springer, 2013
ISBN 10: 1461472016ISBN 13: 9781461472018
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Book Print on Demand
Condition: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book.
Published by Springer New York Jun 2013, 2013
ISBN 10: 1461472016ISBN 13: 9781461472018
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Book Print on Demand
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 160 pp. Englisch.
Published by Springer New York Aug 2015, 2015
ISBN 10: 1489992006ISBN 13: 9781489992000
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Book Print on Demand
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 160 pp. Englisch.
Published by Springer, 2015
ISBN 10: 1489992006ISBN 13: 9781489992000
Seller: booksXpress, Bayonne, NJ, U.S.A.
Book
Soft Cover. Condition: new.
Published by Springer New York, 2013
ISBN 10: 1461472016ISBN 13: 9781461472018
Seller: moluna, Greven, Germany
Book Print on Demand
Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Outlines recent theoretical advances and algorithmic innovations conducted in trust-based collective view predictionAnalyzes the existing vulnerabilities of the content-based recommendation and collaborative filtering techniques, and proposes new,.
Published by Springer New York, 2015
ISBN 10: 1489992006ISBN 13: 9781489992000
Seller: moluna, Greven, Germany
Book Print on Demand
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Outlines recent theoretical advances and algorithmic innovations conducted in trust-based collective view predictionAnalyzes the existing vulnerabilities of the content-based recommendation and collaborative filtering techniques, and proposes new,.
Published by Springer-Verlag New York Inc, 2013
ISBN 10: 1461472016ISBN 13: 9781461472018
Seller: Revaluation Books, Exeter, United Kingdom
Book
Hardcover. Condition: Brand New. 146 pages. 9.75x6.75x0.50 inches. In Stock.
Published by Springer New York, 2013
ISBN 10: 1461472016ISBN 13: 9781461472018
Seller: AHA-BUCH GmbH, Einbeck, Germany
Book
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 tointegrate these techniques into new applications.
Published by Springer New York, 2015
ISBN 10: 1489992006ISBN 13: 9781489992000
Seller: AHA-BUCH GmbH, Einbeck, Germany
Book
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 tointegrate these techniques into new applications.
Published by Springer-Verlag New York Inc., 2015
ISBN 10: 1489992006ISBN 13: 9781489992000
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Book Print on Demand
Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Published by Springer-Verlag New York Inc., 2013
ISBN 10: 1461472016ISBN 13: 9781461472018
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Book Print on Demand
Hardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Published by Springer, 2013
ISBN 10: 1461472016ISBN 13: 9781461472018
Seller: dsmbooks, Liverpool, United Kingdom
Book
Hardcover. Condition: Like New. Like New. book.
Published by Springer, 2015
ISBN 10: 1489992006ISBN 13: 9781489992000
Seller: Mispah books, Redhill, SURRE, United Kingdom
Book
Paperback. Condition: Like New. Like New. book.