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ISBN 10: 6207843339 ISBN 13: 9786207843336
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Lingua: Portoghese
Editore: Edições Nosso Conhecimento, 2024
ISBN 10: 6207960866 ISBN 13: 9786207960866
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Aggiungi al carrelloCondizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | 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.
Condizione: New. pp. 160.
Lingua: Inglese
Editore: Springer-Verlag New York Inc, 2013
ISBN 10: 1461472016 ISBN 13: 9781461472018
Da: Revaluation Books, Exeter, Regno Unito
EUR 154,60
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Trust-based Collective View Prediction | Tiejian Luo (u. a.) | Taschenbuch | xi | Englisch | 2015 | Springer | EAN 9781489992000 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer New York, Springer US, 2013
ISBN 10: 1461472016 ISBN 13: 9781461472018
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 109,94
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Aggiungi al carrelloBuch. Condizione: 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.
EUR 112,77
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
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Aggiungi al carrelloCondizione: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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.
Condizione: New. pp. 146.
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Lingua: Portoghese
Editore: Edições Nosso Conhecimento, 2024
ISBN 10: 6207960866 ISBN 13: 9786207960866
Da: Ria Christie Collections, Uxbridge, Regno Unito
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Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6207843339 ISBN 13: 9786207843336
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ISBN 10: 6207843339 ISBN 13: 9786207843336
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Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2024
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