%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 }