- Tytuł:
- A k-Nearest Neighbors Method for Classifying User Sessions in E-Commerce Scenario
- Autorzy:
-
Suchacka, G.
Skolimowska-Kulig, M.
Potempa, A. - Powiązania:
- https://bibliotekanauki.pl/articles/308645.pdf
- Data publikacji:
- 2015
- Wydawca:
- Instytut Łączności - Państwowy Instytut Badawczy
- Tematy:
-
data mining
e-commerce
k-Nearest Neighbors
k-NN
log file analysis
online store
R-project
supervised classification
web mining
Web store
Web traffic
Web usage mining - 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%.
- Źródło:
-
Journal of Telecommunications and Information Technology; 2015, 3; 64-69
1509-4553
1899-8852 - Pojawia się w:
- Journal of Telecommunications and Information Technology
- Dostawca treści:
- Biblioteka Nauki
Artykuł