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.