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Context-Aware Ranking with Factorization Models
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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'.

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EAN (ISBN-13): 9783642168970
ISBN (ISBN-10): 3642168973
Version reliée
Date de parution: 2010
Editeur: Springer Berlin
180 Pages
Poids: 0,432 kg
Langue: eng/Englisch

Livre dans la base de données depuis 2008-06-24T10:14:52+02:00 (Zurich)
Page de détail modifiée en dernier sur 2023-06-27T02:06:56+02:00 (Zurich)
ISBN/EAN: 9783642168970

ISBN - Autres types d'écriture:
3-642-16897-3, 978-3-642-16897-0
Autres types d'écriture et termes associés:
Auteur du livre: steffen, rendle
Titre du livre: context, models, ranking, intelligence studies

Données de l'éditeur

Auteur: Steffen Rendle
Titre: Studies in Computational Intelligence; Context-Aware Ranking with Factorization Models
Editeur: Springer; Springer Berlin
180 Pages
Date de parution: 2010-11-11
Berlin; Heidelberg; DE
Imprimé / Fabriqué en
Langue: Anglais
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
POD
XII, 180 p.

BB; Hardcover, Softcover / Technik/Allgemeines, Lexika; Künstliche Intelligenz; Verstehen; Computational Intelligence; Context-aware Ranking; Factorization Models; Recommender Systems; Computational Intelligence; Artificial Intelligence; EA; BC

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'.
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 field

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