@MASTERSTHESIS\{IMM2013-06549,
    author       = "S. L. Schmidt",
    title        = "Automated Indexing of Danish Online Shops",
    year         = "2013",
    school       = "Technical University of Denmark, {DTU} Compute, {E-}mail: compute@compute.dtu.dk",
    address      = "Matematiktorvet, Building 303{-B,} {DK-}2800 Kgs. Lyngby, Denmark",
    type         = "",
    note         = "{DTU} supervisor: Finn {\AA}rup Nielsen, faan@dtu.dk, {DTU} Compute. Thesis not publicly available.",
    url          = "http://www.compute.dtu.dk/English.aspx",
    abstract     = "This thesis presents an approach to automated discovery of online shops on the Internet. A prototype system with web crawling and web page classification functionality based on supervised machine learning is implemented in the Python programming language.
The Navne \& Numre Erhverv (NNE) corporate register is proposed as a source of company websites that could potentially be online shops, and therefore should be considered for crawling and classification.
The positive and negative classification training datasets are obtained differently. Existing e-m{\ae}rket member shops are used as the positively labeled data. The negative dataset (non-shop websites) is obtained from the Open Directory Project. 
Two separate supervised classifiers are used: naïve Bayes with Bernoulli eventmodel and decision tree. The use of these classifiers was based on the implementations in the Scikit-learn Python package. The implemented prototype system showed promising results in shop classification with average F1-scores of 0.94 for the Bernoulli naïve Bayes classifier and 0.95 for the decision tree classifier.
A number of recommendations of further work and improvements are given, which should be considered by e-m{\ae}rket, if they are to pursue this approach further."
}