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

• Published in: Journal of Telecommunications and Information Technology 2015 (3), p.64-69
• Main Authors: Suchacka, Grazyna, Skolimowska-Kulig, Magdalena, Potempa, Aneta
• Format: Article
• Language: English
• Subjects: Buying; Classification; Classifiers; data mining; e-commerce; Information technology; Internet; k-Nearest Neighbors; k-NN; log file analysis; Mathematical analysis; online store; Stores; Vectors (mathematics)
• ISSN: 1509-4553; 1899-8852
• DOI: 10.26636/jtit.2015.3.971

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%.