A k-Nearest Neighbors Method for Classifying User Sessions in E-Commerce Scenario

• Autorzy: Suchacka G. , Skolimowska-Kulig M. , Potempa A.
• Języki publikacji: EN

Abstrakty

EN

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%.

Słowa kluczowe

EN

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

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2015
• Tom: nr 3
• Strony: 64--69
• Opis fizyczny: Bibliogr. 25 poz., rys.

Twórcy

autor

Suchacka G.

• Institute of Mathematics and Informatics, Opole University, Opole, Poland

autor

Skolimowska-Kulig M.

• Institute of Mathematics and Informatics, Opole University, Opole, Poland

autor

Potempa A.

• GEA Technika Cieplna Sp. z o.o., Opole, Poland

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