Mobile advertising complements the Internet and interactive television advertising and makes it possible for advertisers to create tailor-made campaigns targeting users according to where they are, their needs of the moment and the devices they are using (i.e. contextualized mobile advertising). Therefore, it is necessary that a fully personalized mobile advertising infrastructure be made. In this paper, we present such a personalized contextualized mobile advertising infrastructure for the advertisement of commercial/non-commercial activities. We name this infrastructure MALCR, in which the primary ingredient is a recommendation mechanism that is supported by the following concepts: (1) minimize users' inputs (a typical interaction metaphor for mobile devices) for implicit browsing behaviors to be best utilized; (2) implicit browsing behaviors are then analyzed with a view to understanding the users' interests in the values of features of advertisements; (3) having understood the users' interests, Mobile Ads relevant to a designated location are subsequently scored and ranked; (4) Top-N scored advertisements are recommended. The recommendation mechanism is novel in its combination of two-level Neural Network learning, Neural Network sensitivity analysis, and attribute-based filtering. This recommendation mechanism is also justified (by thorough evaluations) to show its ability in furnishing effective personalized contextualized mobile advertising.
%0 Journal Article
%1 Yuan2003399
%A Yuan, Soe-Tsyr
%A Tsao, Y. W.
%D 2003
%J Expert Systems with Applications
%K Mobile commerce networks neural recommendation ussn
%N 4
%P 399 - 414
%R DOI: 10.1016/S0957-4174(02)00189-6
%T A recommendation mechanism for contextualized mobile advertising
%U http://www.sciencedirect.com/science/article/B6V03-47RBCJT-1/2/6f5aa281bc7764207a34abd32dbb1782
%V 24
%X Mobile advertising complements the Internet and interactive television advertising and makes it possible for advertisers to create tailor-made campaigns targeting users according to where they are, their needs of the moment and the devices they are using (i.e. contextualized mobile advertising). Therefore, it is necessary that a fully personalized mobile advertising infrastructure be made. In this paper, we present such a personalized contextualized mobile advertising infrastructure for the advertisement of commercial/non-commercial activities. We name this infrastructure MALCR, in which the primary ingredient is a recommendation mechanism that is supported by the following concepts: (1) minimize users' inputs (a typical interaction metaphor for mobile devices) for implicit browsing behaviors to be best utilized; (2) implicit browsing behaviors are then analyzed with a view to understanding the users' interests in the values of features of advertisements; (3) having understood the users' interests, Mobile Ads relevant to a designated location are subsequently scored and ranked; (4) Top-N scored advertisements are recommended. The recommendation mechanism is novel in its combination of two-level Neural Network learning, Neural Network sensitivity analysis, and attribute-based filtering. This recommendation mechanism is also justified (by thorough evaluations) to show its ability in furnishing effective personalized contextualized mobile advertising.
@article{Yuan2003399,
abstract = {Mobile advertising complements the Internet and interactive television advertising and makes it possible for advertisers to create tailor-made campaigns targeting users according to where they are, their needs of the moment and the devices they are using (i.e. contextualized mobile advertising). Therefore, it is necessary that a fully personalized mobile advertising infrastructure be made. In this paper, we present such a personalized contextualized mobile advertising infrastructure for the advertisement of commercial/non-commercial activities. We name this infrastructure MALCR, in which the primary ingredient is a recommendation mechanism that is supported by the following concepts: (1) minimize users' inputs (a typical interaction metaphor for mobile devices) for implicit browsing behaviors to be best utilized; (2) implicit browsing behaviors are then analyzed with a view to understanding the users' interests in the values of features of advertisements; (3) having understood the users' interests, Mobile Ads relevant to a designated location are subsequently scored and ranked; (4) Top-N scored advertisements are recommended. The recommendation mechanism is novel in its combination of two-level Neural Network learning, Neural Network sensitivity analysis, and attribute-based filtering. This recommendation mechanism is also justified (by thorough evaluations) to show its ability in furnishing effective personalized contextualized mobile advertising.},
added-at = {2009-08-25T15:19:00.000+0200},
author = {Yuan, Soe-Tsyr and Tsao, Y. W.},
biburl = {https://www.bibsonomy.org/bibtex/2205f8afe9eacbb15bfd74d90baa83e09/mediadigits},
description = {The use of neural networks.},
doi = {DOI: 10.1016/S0957-4174(02)00189-6},
file = {:RecommendationMechanism.pdf:PDF},
groups = {public},
interhash = {3a22a336ed2bd11b5e35049904bdbce1},
intrahash = {205f8afe9eacbb15bfd74d90baa83e09},
issn = {0957-4174},
journal = {Expert Systems with Applications},
keywords = {Mobile commerce networks neural recommendation ussn},
number = 4,
pages = {399 - 414},
timestamp = {2011-03-13T18:55:34.000+0100},
title = {A recommendation mechanism for contextualized mobile advertising},
url = {http://www.sciencedirect.com/science/article/B6V03-47RBCJT-1/2/6f5aa281bc7764207a34abd32dbb1782},
username = {mediadigits},
volume = 24,
year = 2003
}