| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 385428 | Expert Systems with Applications | 2011 | 7 Pages |
In this article, we deal with the problem of measuring the importance of features, that determine the purchase of the product after being exposed to an advertisement. We use an algorithm called Monte Carlo feature selection, which is based on multiple usage of decision trees, to achieve a ranking of variables from the questionnaire data. Our data generation process relies on low-involvement during the advertisement watching phase and the comparison of advertised products is based on purchase in a virtual shop.
► We measure the importance of features that determine the purchase of products. ► Monte Carlo feature selection algorithm, based on many decision trees, is used. ► The result of the algorithm is a ranking of variables from the questionnaire data. ► Data generation process relies on low-involvement and purchase in a virtual shop.
