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Recommender Systems for Social Tagging Systems (SpringerBriefs in Electrical and Computer Engineering)
Book 14 of 209: SpringerBriefs in Electrical and Computer EngineeringUS$ 65.74
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Recommender Systems for Social Tagging Systems
Book 14 of 209: SpringerBriefs in Electrical and Computer EngineeringSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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Recommender Systems for Social Tagging Systems
Book 14 of 209: SpringerBriefs in Electrical and Computer EngineeringSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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Context-Aware Ranking with Factorization Models (Studies in Computational Intelligence, 330)
Book 24 of 538: Studies in Computational IntelligenceSeller: Ria Christie Collections, Uxbridge, United Kingdom
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Context-Aware Ranking with Factorization Models (Studies in Computational Intelligence, 330)
Book 24 of 538: Studies in Computational IntelligenceSeller: Ria Christie Collections, Uxbridge, United Kingdom
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Context-Aware Ranking With Factorization Models
Book 24 of 538: Studies in Computational IntelligenceSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the ¿noise¿ that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.
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Context-Aware Ranking With Factorization Models
Book 24 of 538: Studies in Computational IntelligenceSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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Context-Aware Ranking with Factorization Models
Book 24 of 538: Studies in Computational IntelligenceLanguage: English
Published by Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE, 2010
ISBN 10: 3642168973 ISBN 13: 9783642168970
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Add to basketHardback. Condition: New. 2011 ed.
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Context-Aware Ranking with Factorization Models
Book 24 of 538: Studies in Computational IntelligenceLanguage: English
Published by Springer Berlin Heidelberg, 2014
ISBN 10: 3642423973 ISBN 13: 9783642423970
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Context-aware ranking is an important task with many applications. E.g. in recommender systems items (products, movies, .) and for search engines webpages should be ranked. In all these applications, the ranking is not global (i.e. always the same) but depends on the context. Simple examples for context are the user for recommender systems and the query for search engines. More complicated context includes time, last actions, etc. The major problem is that typically the variable domains (e.g. customers, products) are categorical and huge, the observations are very sparse and only positive events are observed. In this book, a generic method for context-aware ranking as well as its application are presented. For modelling a new factorization based on pairwise interactions is proposed and compared to other tensor factorization approaches. For learning, the `Bayesian Context-aware Ranking' framework consisting of an optimization criterion and algorithm is developed. The second main part of the book applies this general theory to the three scenarios of item, tag and sequential-set recommendation. Furthermore extensions of time-variant factors and one-class problems are studied. This book generalizes and builds on work that has received the `WWW 2010 Best Paper Award', the `WSDM 2010 Best Student Paper Award' and the `ECML/PKDD 2009 Best Discovery Challenge Award'.
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Context-Aware Ranking with Factorization Models
Book 24 of 538: Studies in Computational IntelligenceLanguage: English
Published by Springer-Verlag GmbH, 2010
ISBN 10: 3642168973 ISBN 13: 9783642168970
Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
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Context-Aware Ranking with Factorization Models
Book 24 of 538: Studies in Computational IntelligenceLanguage: English
Published by Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE, 2010
ISBN 10: 3642168973 ISBN 13: 9783642168970
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Add to basketHardback. Condition: New. 2011 ed. Context-aware ranking is an important task with many applications. E.g. in recommender systems items (products, movies, .) and for search engines webpages should be ranked. In all these applications, the ranking is not global (i.e. always the same) but depends on the context. Simple examples for context are the user for recommender systems and the query for search engines. More complicated context includes time, last actions, etc. The major problem is that typically the variable domains (e.g. customers, products) are categorical and huge, the observations are very sparse and only positive events are observed. In this book, a generic method for context-aware ranking as well as its application are presented. For modelling a new factorization based on pairwise interactions is proposed and compared to other tensor factorization approaches. For learning, the `Bayesian Context-aware Ranking' framework consisting of an optimization criterion and algorithm is developed. The second main part of the book applies this general theory to the three scenarios of item, tag and sequential-set recommendation. Furthermore extensions of time-variant factors and one-class problems are studied. This book generalizes and builds on work that has received the `WWW 2010 Best Paper Award', the `WSDM 2010 Best Student Paper Award' and the `ECML/PKDD 2009 Best Discovery Challenge Award'.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find th.
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Context-Aware Ranking with Factorization Models
Book 24 of 538: Studies in Computational IntelligenceLanguage: English
Published by Springer Berlin Heidelberg Okt 2014, 2014
ISBN 10: 3642423973 ISBN 13: 9783642423970
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Context-aware ranking is an important task with many applications. E.g. in recommender systems items (products, movies, .) and for search engines webpages should be ranked. In all these applications, the ranking is not global (i.e. always the same) but depends on the context. Simple examples for context are the user for recommender systems and the query for search engines. More complicated context includes time, last actions, etc. The major problem is that typically the variable domains (e.g. customers, products) are categorical and huge, the observations are very sparse and only positive events are observed. In this book, a generic method for context-aware ranking as well as its application are presented. For modelling a new factorization based on pairwise interactions is proposed and compared to other tensor factorization approaches. For learning, the `Bayesian Context-aware Ranking' framework consisting of an optimization criterion and algorithm is developed. The second main part of the book applies this general theory to the three scenarios of item, tag and sequential-set recommendation. Furthermore extensions of time-variant factors and one-class problems are studied. This book generalizes and builds on work that has received the `WWW 2010 Best Paper Award', the `WSDM 2010 Best Student Paper Award' and the `ECML/PKDD 2009 Best Discovery Challenge Award'. 192 pp. Englisch.
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Context-Aware Ranking with Factorization Models
Book 24 of 538: Studies in Computational IntelligenceLanguage: English
Published by Springer Berlin Heidelberg, 2014
ISBN 10: 3642423973 ISBN 13: 9783642423970
US$ 111.48
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents a unified theory of context-aware ranking that subsumes several recommendation tasks such as item, tag and context-aware recommendation Easily readable and understandable Written by an expert in the fieldPresents a unifi.
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Context-Aware Ranking with Factorization Models
Book 24 of 538: Studies in Computational IntelligenceLanguage: English
Published by Springer Berlin Heidelberg, 2010
ISBN 10: 3642168973 ISBN 13: 9783642168970
US$ 112.36
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Ships from Germany to U.S.A.Quantity: Over 20 available
Add to basketGebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents a unified theory of context-aware ranking that subsumes several recommendation tasks such as item, tag and context-aware recommendation Easily readable and understandable Written by an expert in the fieldContext-aware ranki.
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Context-Aware Ranking with Factorization Models
Book 24 of 538: Studies in Computational IntelligenceUS$ 167.38
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Condition: New. PRINT ON DEMAND.
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Context-Aware Ranking with Factorization Models
Book 24 of 538: Studies in Computational IntelligenceLanguage: English
Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg Okt 2014, 2014
ISBN 10: 3642423973 ISBN 13: 9783642423970
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Context-aware ranking is an important task with many applications. E.g. in recommender systems items (products, movies, .) and for search engines webpages should be ranked. In all these applications, the ranking is not global (i.e. always the same) but depends on the context. Simple examples for context are the user for recommender systems and the query for search engines. More complicated context includes time, last actions, etc. The major problem is that typically the variable domains (e.g. customers, products) are categorical and huge, the observations are very sparse and only positive events are observed. In this book, a generic method for context-aware ranking as well as its application are presented. For modelling a new factorization based on pairwise interactions is proposed and compared to other tensor factorization approaches. For learning, the `Bayesian Context-aware Ranking' framework consisting of an optimization criterion and algorithm is developed. The second main part of the book applies this general theory to the three scenarios of item, tag and sequential-set recommendation. Furthermore extensions of time-variant factors and one-class problems are studied. This book generalizes and builds on work that has received the `WWW 2010 Best Paper Award', the `WSDM 2010 Best Student Paper Award' and the `ECML/PKDD 2009 Best Discovery Challenge Award'.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 192 pp. Englisch.
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Taschenbuch. Condition: Neu. Context-Aware Ranking with Factorization Models | Steffen Rendle | Taschenbuch | xii | Englisch | 2014 | Springer | EAN 9783642423970 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
