Tytuł:

A k-Nearest Neighbors Method for Classifying User Sessions in E-Commerce Scenario, Journal of Telecommunications and Information Technology, 2015, nr 3

Autor:

Potempa, Aneta ; Skolimowska-Kulig, Magdalena ; Suchacka, Grażyna

Temat i słowa kluczowe:

data mining ; e-commerce ; k-Nearest Neighbors ; Web traffic ; log file analysis ; online store ; R-project ; Web store ; k-NN ; Web usage mining ; supervised classification

Opis:

This paper addresses the problem of classification of user sessions in an online store into two classes: buying sessions (during which a purchase confirmation occurs) and browsing sessions. As interactions connected with a purchase confirmation are typically completed at the end of user sessions, some information describing active sessions may be observed and used to assess the probability of making a purchase. The authors formulate the problem of predicting buying sessions in a Web store as a supervised classification problem where there are two target classes, connected with the fact of finalizing a purchase transaction in session or not, and a feature vector containing some variables describing user sessions. The presented approach uses the k-Nearest Neighbors (k-NN) classification. Based on historical data obtained from online bookstore log files a k-NN classifier was built and its efficiency was verified for different neighborhood sizes. A 11-NN classifier was the most effective both in terms of buying session predictions and overall predictions, achieving sensitivity of 87.5% and accuracy of 99.85%.

Wydawca:

National Institute of Telecommunications

Data wydania:

2015, nr 3

Typ zasobu:

artykuł

Format:

application/pdf

Identyfikator zasobu:

ISSN 1509-4553, on-line: ISSN 1899-8852

Źródło:

Journal of Telecommunications and Information Technology

Język:

ang

Prawa:

Biblioteka Naukowa Instytutu Łączności