| Title: | Mining user daily behavior patterns from access logs of massive software and websites |
| Author: | Wei, Zhao (1)
; Jie, Liu (2)
; Dan, Ye (2)
; Jun, Wei (2)
|
| Conference Name: | 5th Asia-Pacific Symposium on Internetware, Internetware 2013
|
| Conference Date: | October 23, 2013 - October 24, 2013
|
| Issued Date: | 2013
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| Conference Place: | Changsha, China
|
| Publish Place: | Association for Computing Machinery, General Post Office, P.O. Box 30777, NY 10087-0777, United States
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| Indexed Type: | EI
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| ISBN: | 9781450323697
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| 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
|
| 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. |
| 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. |
| Language: | 英语
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| Content Type: | 会议论文
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| URI: | http://ir.iscas.ac.cn/handle/311060/16676
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| Appears in Collections: | 软件所图书馆_会议论文
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| Recommended Citation: | |
Wei, Zhao ,Jie, Liu ,Dan, Ye ,et al. Mining user daily behavior patterns from access logs of massive software and websites[C]. 见:5th Asia-Pacific Symposium on Internetware, Internetware 2013. Changsha, China. October 23, 2013 - October 24, 2013.
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