Context Aware Ranking Factorization Models by Rendle Steffen (21 results)
Language: English
Published by Springer, 2014
Series: Book 24 of 538 - Studies in Computational Intelligence
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Language: English
Published by Springer, 2010
Series: Book 24 of 538 - Studies in Computational Intelligence
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Language: English
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Published by Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE, 2010
Series: Book 24 of 538 - Studies in Computational Intelligence
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Hardback. 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 fo…r 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'.
Language: English
Published by Springer, 2014
Series: Book 24 of 538 - Studies in Computational Intelligence
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Published by Springer, 2014
Series: Book 24 of 538 - Studies in Computational Intelligence
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Taschenbuch. Condition: Neu. Context-Aware Ranking with Factorization Models | Steffen Rendle | Taschenbuch | Studies in Computational Intelligence | xii | Englisch | 2014 | Springer | EAN 9783642423970 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springe…r[dot]com | Anbieter: preigu.
Language: English
Published by Springer Berlin Heidelberg, 2014
Series: Book 24 of 538 - Studies in Computational Intelligence
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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'.
Language: English
Published by Springer-Verlag GmbH, 2010
Series: Book 24 of 538 - Studies in Computational Intelligence
- Hardcover
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Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
Language: English
Published by Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE, 2010
Series: Book 24 of 538 - Studies in Computational Intelligence
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Hardback. Condition: New. 2011 ed.
Language: English
Published by Springer, 2014
Series: Book 24 of 538 - Studies in Computational Intelligence
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Language: English
Published by Springer, 2010
Series: Book 24 of 538 - Studies in Computational Intelligence
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Language: English
Published by Springer Berlin Heidelberg Okt 2014, 2014
Series: Book 24 of 538 - Studies in Computational Intelligence
- Softcover
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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.
Language: English
Published by Springer Berlin Heidelberg, 2014
Series: Book 24 of 538 - Studies in Computational Intelligence
- Softcover
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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 fieldPres…ents a unifi.
Language: English
Published by Springer Berlin Heidelberg, 2010
Series: Book 24 of 538 - Studies in Computational Intelligence
- Hardcover
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Gebunden. 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.
Language: English
Published by Springer, 2014
Series: Book 24 of 538 - Studies in Computational Intelligence
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Language: English
Published by Springer, 2014
Series: Book 24 of 538 - Studies in Computational Intelligence
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Language: English
Published by Springer, Springer Okt 2014, 2014
Series: Book 24 of 538 - Studies in Computational Intelligence
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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. alwa…ys 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 KG, Sachsenplatz 4-6, 1201 Wien 192 pp. Englisch.
