Institutional Repository
| Mining user daily behavior patterns from access logs of massive software and websites | |
| Wei, Zhao (1); Jie, Liu (2); Dan, Ye (2); Jun, Wei (2) | |
| 2013 | |
| Conference Name | 5th Asia-Pacific Symposium on Internetware, Internetware 2013 |
| Conference Date | October 23, 2013 - October 24, 2013 |
| Conference Place | Changsha, China |
| Indexed Type | EI |
| Publish Place | Association for Computing Machinery, General Post Office, P.O. Box 30777, NY 10087-0777, United States |
| ISBN | 9781450323697 |
| Department | (1) Institute of Software, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing 100190, China; (2) Chinese Academy of Sciences, Institute of Software, Beijing 100190, China |
| English Abstract | Everyone has a characteristic pattern of daily activities. This study applies cluster analysis to identify a computer user's daily behavior patterns based on 1000 China users' 4-weeks software and web usage. Clustering models are built for 4 different behavior definition methods with different time period divisions and feature measurement selections. With these patterns, we build classification models to predict new users' daily behavior pattern with their half day activity logs. For example, if we know one user use computer for entertainment in the morning, we can predict his behavior in the afternoon and evening. The prediction model can be used to recommend suitable items to users according to their current behavior status. Our method can get 92.5% prediction correctness for the best.; Everyone has a characteristic pattern of daily activities. This study applies cluster analysis to identify a computer user's daily behavior patterns based on 1000 China users' 4-weeks software and web usage. Clustering models are built for 4 different behavior definition methods with different time period divisions and feature measurement selections. With these patterns, we build classification models to predict new users' daily behavior pattern with their half day activity logs. For example, if we know one user use computer for entertainment in the morning, we can predict his behavior in the afternoon and evening. The prediction model can be used to recommend suitable items to users according to their current behavior status. Our method can get 92.5% prediction correctness for the best. |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16676 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Wei, Zhao ,Jie, Liu ,Dan, Ye ,et al. Mining user daily behavior patterns from access logs of massive software and websites[C]. Association for Computing Machinery, General Post Office, P.O. Box 30777, NY 10087-0777, United States,2013. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment