Abstract
Web hosting companies strive to provide customised customer services and want to know the commercial intent of a website. Whether a website is run by an individual person, a company, a non-profit organisation, or a public institution constitutes a great challenge in website classification as website content might be sparse. In this paper, we present a novel approach for determining the commercial intent of websites by using both supervised and unsupervised machine learning algorithms. Based on a large real-world data set, we evaluate our model with respect to its effectiveness and efficiency and observe the best performance with a multilayer perceptron.
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Notes
- 1.
This work was carried out in cooperation with the web hosting company 1&1 IONOS.
- 2.
- 3.
https://dmoz-odp.org/World/Deutsch/, accessed on 2019-10-24.
- 4.
- 5.
http://www.npo-manager.de/vereine/, accessed on 2019-10-24.
- 6.
http://www.schulliste.eu/, accessed on 2019-10-24.
- 7.
We remove unavailable domains or domain parking pages, i.e., websites with default content provided by the domain name registar.
- 8.
We consider only static visible textual information as input for classification, hence no HTML markups, meta tags or JavaScript.
- 9.
The data sets are freely available for research purposes at https://github.com/michaelfaerber/website-classification/.
- 10.
We published the confusion matrices for each model at https://github.com/michaelfaerber/website-classification/.
- 11.
“Blogs” fall under the categories of private or company according to our defined classes from Sect. 3.
- 12.
This is a subset of our company class.
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Färber, M., Scheer, B., Bartscherer, F. (2020). Who’s Behind That Website? Classifying Websites by the Degree of Commercial Intent. In: Bielikova, M., Mikkonen, T., Pautasso, C. (eds) Web Engineering. ICWE 2020. Lecture Notes in Computer Science(), vol 12128. Springer, Cham. https://doi.org/10.1007/978-3-030-50578-3_10
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