Search for: [Description = "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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