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Kleinster Preis: 120.08 EUR, größter Preis: 139.09 EUR, Mittelwert: 135.29 EUR
Context-Aware Ranking with Factorization Models - Steffen Rendle
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Context-Aware Ranking with Factorization Models - neues Buch

2010, ISBN: 9783642168970

[ED: Buch], [PU: Springer-Verlag GmbH], 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'. -, [SC: 0.00]

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Context-Aware Ranking with Factorization Models - Steffen Rendle
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Steffen Rendle:

Context-Aware Ranking with Factorization Models - neues Buch

2010, ISBN: 9783642168970

[ED: Buch], [PU: Springer-Verlag GmbH], 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'., [SC: 0.00]

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Context-Aware Ranking with Factorization Models - Steffen Rendle
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Steffen Rendle:
Context-Aware Ranking with Factorization Models - neues Buch

2010

ISBN: 9783642168970

[ED: Buch], [PU: Springer-Verlag GmbH], 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'., [SC: 4.50]

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Context-Aware Ranking with Factorization Models - Steffen Rendle
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Steffen Rendle:
Context-Aware Ranking with Factorization Models - neues Buch

2010, ISBN: 9783642168970

[ED: Buch], [PU: Springer-Verlag GmbH], 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'., [SC: 0.00]

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ISBN: 9783642168970

Hardback, [PU: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG], Context-aware ranking is an important task in search engine ranking. This book presents a generic method for context-aware ranking as well as its application. It applies this general theory to the three scenarios of item, tag and sequential-set recommendation., Engineering: General

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Details zum Buch
Context-Aware Ranking with Factorization Models
Autor:

Rendle, Steffen

Titel:

Context-Aware Ranking with Factorization Models

ISBN-Nummer:

9783642168970

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'.

Detailangaben zum Buch - Context-Aware Ranking with Factorization Models


EAN (ISBN-13): 9783642168970
ISBN (ISBN-10): 3642168973
Gebundene Ausgabe
Erscheinungsjahr: 2010
Herausgeber: Springer-Verlag GmbH
180 Seiten
Gewicht: 0,432 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 24.06.2008 10:14:52
Buch zuletzt gefunden am 01.04.2016 02:37:00
ISBN/EAN: 9783642168970

ISBN - alternative Schreibweisen:
3-642-16897-3, 978-3-642-16897-0

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