User Behavior Detection for Online Survey via Sequential Pattern Mining
With the rapid development of Internet,online survey becomes an emerging industry.It is a very challenging task to get interesting knowledge from the large-scale behavioral data of respondents.This paper firstly makes reduction of user properties and behavior data from an online survey company,and based on which we construct an online survey user model; then,an improved generalized sequential pattern (GSP) algorithm is proposed to mine frequent sequential patterns; finally,we give an in-depth user behavior analysis of online survey,which is from conventional sequential patterns of user behavior,sequential patterns based on specific behavior and time window,and user behavior prediction.The experimental results show that it is effective to analyze the sequence of user behavior thorough improved GSP algorithm.Compared with the classical GSP algorithm,user behavior prediction accuracy rate increases 19% via our proposed sequential pattern analysis approach.
Online survey Sequence analysis User behavior prediction GSP algorithm
Xiaowei Zhu Shaochun Wu Guobing Zou
School of Computer Engineering and Science, Shanghai University Shanghai 200444, China
国际会议
秦皇岛
英文
493-497
2015-09-18（万方平台首次上网日期，不代表论文的发表时间）