Mining User Daily Behavior Patterns from Access Logs of Massive Software and Websites
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.
Behavior Pattern Data Mining Behavior Prediction Cluster Classification
Wei Zhao Jie Liu Dan Ye Jun Wei
University of Chinese Academy of Sciences Institute of Software,ChineseAcademy of Sciences Beijing 1 Institute of Software,ChineseAcademy of Sciences Beijing 100190,China Institute of Software,Chinese Academy of Sciences Beijing 100190,China
国际会议
长沙
英文
203-206
2013-10-23（万方平台首次上网日期，不代表论文的发表时间）