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

Authors

DOI:

https://doi.org/10.26636/jtit.2015.3.971

Keywords:

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

Abstract

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%.

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Submitted

2023-06-26

Published

2015-09-30

Issue

No. 3 (2015)

Section

RESEARCH ARTICLES

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Copyright (c) 2015 Journal of Telecommunications and Information Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
G. Suchacka, M. Skolimowska-Kulig, and A. Potempa, “A k-Nearest Neighbors Method for Classifying User Sessions in E-Commerce Scenario”, JTIT, vol. 61, no. 3, pp. 64–69, Sep. 2015, doi: 10.26636/jtit.2015.3.971.