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Edité par Morgan & Claypool Publishers, 2014
ISBN 10 : 1627052577ISBN 13 : 9781627052573
Vendeur : suffolkbooks, Center moriches, NY, Etats-Unis
Livre
Etat : VeryGood. Fast Shipping - Safe and Secure 7 days a week!.
Edité par Morgan & Claypool Publishers, 2014
ISBN 10 : 1627052577ISBN 13 : 9781627052573
Vendeur : WeBuyBooks, Rossendale, LANCS, Royaume-Uni
Livre
Etat : Good. Most items will be dispatched the same or the next working day.
Edité par Springer, 2014
ISBN 10 : 3031007786ISBN 13 : 9783031007781
Vendeur : booksXpress, Bayonne, NJ, Etats-Unis
Livre
Soft Cover. Etat : new.
Edité par Springer, 2014
ISBN 10 : 3031007786ISBN 13 : 9783031007781
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
Livre
Etat : New.
Edité par Springer, 2014
ISBN 10 : 3031007786ISBN 13 : 9783031007781
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Livre
Etat : New.
Edité par Springer, 2014
ISBN 10 : 3031007786ISBN 13 : 9783031007781
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Livre
Etat : As New. Unread book in perfect condition.
Edité par Springer, 2014
ISBN 10 : 3031007786ISBN 13 : 9783031007781
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Livre impression à la demande
Etat : New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book.
Edité par Springer 2014-05, 2014
ISBN 10 : 3031007786ISBN 13 : 9783031007781
Vendeur : Chiron Media, Wallingford, Royaume-Uni
Livre
PF. Etat : New.
Edité par Springer, 2014
ISBN 10 : 3031007786ISBN 13 : 9783031007781
Vendeur : GreatBookPricesUK, Castle Donington, DERBY, Royaume-Uni
Livre
Etat : New.
Edité par Springer International Publishing Mai 2014, 2014
ISBN 10 : 3031007786ISBN 13 : 9783031007781
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Livre impression à la demande
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The importance of accurate recommender systems has been widely recognized by academia and industry, and recommendation is rapidly becoming one of the most successful applications of data mining and machine learning. Understanding and predicting the choices and preferences of users is a challenging task: real-world scenarios involve users behaving in complex situations, where prior beliefs, specific tendencies, and reciprocal influences jointly contribute to determining the preferences of users toward huge amounts of information, services, and products. Probabilistic modeling represents a robust formal mathematical framework to model these assumptions and study their effects in the recommendation process. This book starts with a brief summary of the recommendation problem and its challenges and a review of some widely used techniques Next, we introduce and discuss probabilistic approaches for modeling preference data. We focus our attention on methods based on latent factors, such as mixture models, probabilistic matrix factorization, and topic models, for explicit and implicit preference data. These methods represent a significant advance in the research and technology of recommendation. The resulting models allow us to identify complex patterns in preference data, which can be exploited to predict future purchases effectively. The extreme sparsity of preference data poses serious challenges to the modeling of user preferences, especially in the cases where few observations are available. Bayesian inference techniques elegantly address the need for regularization, and their integration with latent factor modeling helps to boost the performances of the basic techniques. We summarize the strengths and weakness of several approaches by considering two different but related evaluation perspectives, namely, rating prediction and recommendation accuracy. Furthermore, we describe how probabilistic methods based on latent factors enable the exploitation of preference patterns in novel applications beyond rating prediction or recommendation accuracy. We finally discuss the application of probabilistic techniques in two additional scenarios, characterized by the availability of side information besides preference data. In summary, the book categorizes the myriad probabilistic approaches to recommendations and provides guidelines for their adoption in real-world situations. 200 pp. Englisch.
Edité par Springer, 2014
ISBN 10 : 3031007786ISBN 13 : 9783031007781
Vendeur : GreatBookPricesUK, Castle Donington, DERBY, Royaume-Uni
Livre
Etat : As New. Unread book in perfect condition.
Edité par Springer International Publishing, 2014
ISBN 10 : 3031007786ISBN 13 : 9783031007781
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Livre
Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - The importance of accurate recommender systems has been widely recognized by academia and industry, and recommendation is rapidly becoming one of the most successful applications of data mining and machine learning. Understanding and predicting the choices and preferences of users is a challenging task: real-world scenarios involve users behaving in complex situations, where prior beliefs, specific tendencies, and reciprocal influences jointly contribute to determining the preferences of users toward huge amounts of information, services, and products. Probabilistic modeling represents a robust formal mathematical framework to model these assumptions and study their effects in the recommendation process. This book starts with a brief summary of the recommendation problem and its challenges and a review of some widely used techniques Next, we introduce and discuss probabilistic approaches for modeling preference data. We focus our attention on methods based on latent factors, such as mixture models, probabilistic matrix factorization, and topic models, for explicit and implicit preference data. These methods represent a significant advance in the research and technology of recommendation. The resulting models allow us to identify complex patterns in preference data, which can be exploited to predict future purchases effectively. The extreme sparsity of preference data poses serious challenges to the modeling of user preferences, especially in the cases where few observations are available. Bayesian inference techniques elegantly address the need for regularization, and their integration with latent factor modeling helps to boost the performances of the basic techniques. We summarize the strengths and weakness of several approaches by considering two different but related evaluation perspectives, namely, rating prediction and recommendation accuracy. Furthermore, we describe how probabilistic methods based on latent factors enable the exploitation of preference patterns in novel applications beyond rating prediction or recommendation accuracy. We finally discuss the application of probabilistic techniques in two additional scenarios, characterized by the availability of side information besides preference data. In summary, the book categorizes the myriad probabilistic approaches to recommendations and provides guidelines for their adoption in real-world situations.
Edité par Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2014
ISBN 10 : 3031007786ISBN 13 : 9783031007781
Vendeur : moluna, Greven, Allemagne
Livre impression à la demande
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The importance of accurate recommender systems has been widely recognized by academia and industry, and recommendation is rapidly becoming one of the most successful applications of data mining and machine learning. Understanding and predicting the choices .
Edité par Springer, 2014
ISBN 10 : 3031007786ISBN 13 : 9783031007781
Vendeur : Kennys Bookstore, Olney, MD, Etats-Unis
Livre
Etat : New. 2014. Paperback. . . . . . Books ship from the US and Ireland.
Edité par Springer, 2014
ISBN 10 : 3031007786ISBN 13 : 9783031007781
Vendeur : Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlande
Livre
Etat : New. 2014. Paperback. . . . . .