A k-Nearest Neighbors Method for Classifying User Sessions in E-Commerce Scenario, Journal of Telecommunications and Information Technology, 2015, nr 3

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%

ジリツ ト キョウチョウ オ ウナガス ジコ ジツゲン シエン システム ノ カイハツ ト ヒョウカ

本研究では，学生の自立と協調を促す自己実現支援システムを開発している．自己実現のためには，自立と協調の精神が不可欠である．自分がどう成長したいか，どのような人生としたいかなどの価値観の明確化，信頼できる仲間との切磋琢磨を通じた相乗効果が求められる．本システムは，これまでWebシステムとして開発していたが，学生にとっての利便性や継続性を考慮し，モバイルアプリケーションとして開発したので報告する．Though proactive action is necessary for career building, it’s difficult to behave proactively for several students. The 7 habits is one of powerful schemes for proactive action choice. We are developing proactive action support system by visualization of quadrant II activities called Self-reflector. Self-reflector systemized the first three habits in the 7 habits. However, there were few frequencies for which the examinee uses this system, because this system was not applied to mobile use. Periodic and long-term practice is necessary to gain the significant effect of the 7 habits. In this paper, we apply the system to mobile application for promoting of periodic and long-term use of students, called Self-reflector-Plus. To examine our system, we have designed rubric to evaluate effect of Self-reflector-Plus. There are nine components corresponding habit 1 to 3 in the 7 habits

サーバクラスタでの低消費電力化のための移行モデルの研究

博士(工学)法政大学 (Hosei University

JTIT

kwartalni