• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.1-19
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• 2010

In offline environment, unlike traditional advertising model through TV, newspaper, and radio, online advertising model draws instantaneous responses from potential consumers and it is convenient to assess. This kind of characteristics of Internet advertising model has driven the growth of advertising model among various Internet business models. There are, conventionally classified, CPM (Cost Per Mile), CPC (Cost Per Click), and CPS (Cost Per Sales) models as Internet advertising expenditure model. These can be examined in manners regarding risks that stakeholders should stand and degree of responsibility. CPM model that is based on number of advertisement exposure is mechanically exposed to users but not actually recognized by users resulting in risk of wasted expenditure by advertisers without any advertising effect. While on aspect of media, CPS model that is based on conversion action is the most risky model because of the conversion action such as product purchase is determined by capability of advertisers not that of media. In this regard, while there are issue of CPM and CPS models disadvantageously affecting only one side of Internet advertising business model value network, CPC model has been evaluated as reasonable both to advertisers and media, and occupied the largest segment of Internet advertising market. However, CPC model also can cause fraudulent behavior such as click fraud because of the competition or dishonest amount of advertising expenditure. On the user aspect, unintentionally accessed advertisements can lead to more inappropriate expenditure from advertisers. In this paper, we suggest "CPCD"(Cost Per Coupon Download) model. This goes beyond simple clicking of advertisements and advertising expenditure is exerted when users download a coupon from advertisers, which is a concept in between CPC and CPS models. To achieve the purpose, we describe the scenario of advertiser perspective, processes, participants and their benefits of CPCD model. Especially, we suggest the new value in online coupon; "possibility of storage" and "complement for delivery to the target group". We also analyze the working condition for advertiser by a comparison of CPC and CPCD models through advertising expenditure simulation. The result of simulation implies that the CPCD model suits more properly to advertisers with medium-low price products rather than that of high priced goods. This denotes that since most of advertisers in CPC model are dealing with medium-low priced products, the result is very interesting. At last, we contemplate applicability of CPCD model in ubiquitous environment.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.