Mining user daily behavior patterns from access logs of massive software and websites
Authors: 
Wei Zhao, Jie Liu, Dan Ye, Jun WeiAuthors Info & Claims
Wei Zhao, Jie Liu, Dan Ye, Jun WeiAuthors Info & ClaimsArticle No.: 18, Pages 1 - 4
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.
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- Mining user daily behavior patterns from access logs of massive software and websites
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Information & Contributors
Information
Published In
October 2013
211 pages
ISBN:9781450323697
DOI:10.1145/2532443
- Conference Chairs:
- Hong Mei,
- Jian Lv,
- Program Chair:
- Xiaoguang Mao
Copyright © 2013 ACM.
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Sponsors
- NJU: Nanjing University
- CCF: China Computer Federation
- Chinese Academy of Sciences
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 23 October 2013
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- Research-article
Funding Sources
- Ministry of Science and Technology of the People's Republic of China
- National Key Technology R&D Program
Conference
Internetware '13
Sponsor:
- NJU
- CCF
Internetware '13: The Fifth Asia-Pacific Symposium on Internetware
October 23 - 24, 2013
Changsha, China
Acceptance Rates
Internetware '13 Paper Acceptance Rate 15 of 50 submissions, 30%;
Overall Acceptance Rate 55 of 111 submissions, 50%
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