Context-Aware Ranking with Factorization Models

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 c...

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গ্রন্থ-পঞ্জীর বিবরন
প্রধান লেখক: Rendle, Steffen
বিন্যাস: Livre numérique
ভাষা:Anglais
প্রকাশিত: Berlin, Heidelberg : Springer Berlin Heidelberg [20..].
Cham : Springer Nature
সংস্করন:1st ed. 2011.
মালা:Studies in Computational Intelligence 330
অনলাইন ব্যবহার করুন:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
Accès Université d'Orléans
Accès INSA CVL
টীকা: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
Autres localisations: Voir dans le Sudoc
Edition sous un autre format:• Context-Aware Ranking with Factorization Models, Texte imprimé, 9783642168970
• Context-Aware Ranking with Factorization Models, Texte imprimé, 9783642423970
• Context-Aware Ranking with Factorization Models, Texte imprimé, 9783642168970
• Context-Aware Ranking with Factorization Models, Texte imprimé, 9783642168994
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245 1 0 |a Context-Aware Ranking with Factorization Models   |c by Steffen Rendle. 
250 |a 1st ed. 2011. 
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505 1 |a Part I Overview Part II Theory Part III Application Part IV Extensions Part V Conclusion 
506 |a Accès en ligne pour les établissements français bénéficiaires des licences nationales 
506 |a Accès soumis à abonnement pour tout autre établissement 
506 |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 
520 |a 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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