Advances in wireless technology increase the number of mobile device users and give pace to the rapid development of mobile commerce conducted with these devices. Empowered by the Web¿s interactive and quick-response capabilities, one-to-one mobile marketing is a very promising direct marketing channel. However, the success of mobile commerce largely depends on whether personalization can be utilized to deliver highly personalized and context sensitive information to mobile clients. To realize the personalization function in mobile advertising, Bayesian Network-based user-modeling technique is adopted. We propose that context, content, and user preferences are the important components that can be utilized to achieve personalization effect in mobile advertising application. The collected data of user information from the empirical study is used as prior probabilities for the Bayesian network. The personalized Bayesian Network-based prototype consists of a user model, a context model, a content component and a matching engine to deliver more relevant advertisements to customers through mobile devices.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Vendeur : Chiron Media, Wallingford, Royaume-Uni
PF. Etat : New. This item is printed on demand. Brand new book, sourced directly from publisher. Dispatch time is 6-7 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely. N° de réf. du vendeur 6666-IUK-9783639184983
Quantité disponible : 20 disponible(s)
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Etat : New. N° de réf. du vendeur 6942697-n
Quantité disponible : Plus de 20 disponibles
Vendeur : PBShop.store US, Wood Dale, IL, Etats-Unis
PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. N° de réf. du vendeur L0-9783639184983
Quantité disponible : Plus de 20 disponibles
Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. N° de réf. du vendeur L0-9783639184983
Quantité disponible : Plus de 20 disponibles
Vendeur : California Books, Miami, FL, Etats-Unis
Etat : New. N° de réf. du vendeur I-9783639184983
Quantité disponible : Plus de 20 disponibles
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Etat : New. In English. N° de réf. du vendeur ria9783639184983_new
Quantité disponible : Plus de 20 disponibles
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Etat : New. N° de réf. du vendeur 6942697-n
Quantité disponible : Plus de 20 disponibles
Vendeur : moluna, Greven, Allemagne
Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: XU JingjunJingjun XU, PhD student, Management Information Systems, University of British Columbia, Vancouver, Canada, with over 10 publications in areas of mobile commerce and electronic commerce. Shaoyi LIAO, Associate Professor of. N° de réf. du vendeur 4965100
Quantité disponible : Plus de 20 disponibles
Vendeur : Books Puddle, Woodside, NY, Etats-Unis
Etat : New. pp. 138. N° de réf. du vendeur 26128747981
Quantité disponible : 4 disponible(s)
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Advances in wireless technology increase the number of mobile device users and give pace to the rapid development of mobile commerce conducted with these devices. Empowered by the Web s interactive and quick-response capabilities, one-to-one mobile marketing is a very promising direct marketing channel. However, the success of mobile commerce largely depends on whether personalization can be utilized to deliver highly personalized and context sensitive information to mobile clients. To realize the personalization function in mobile advertising, Bayesian Network-based user-modeling technique is adopted. We propose that context, content, and user preferences are the important components that can be utilized to achieve personalization effect in mobile advertising application. The collected data of user information from the empirical study is used as prior probabilities for the Bayesian network. The personalized Bayesian Network-based prototype consists of a user model, a context model, a content component and a matching engine to deliver more relevant advertisements to customers through mobile devices. N° de réf. du vendeur 9783639184983
Quantité disponible : 2 disponible(s)