Analytical method of web user behavior using Hidden Markov Model

We propose a new analytical method to classify web user behavior based on such latent states of users as intention, interest, or motivation. First, we put the clickstream data of many users into a Hidden Markov Model in which the number of hidden states is large enough to build a state transition network. Since the variable hidden states represent different latent states of users, the movement on the state transition network can represent user behavior. Second, we divide each piece of clickstream data into sessions, which we classify using network movement as feature values. These cluster labels represent the latent states of users during their stay in the web service. In this paper, we applied our method to the data of a social network game named Girl Friend BETA, which is an online game that is mainly provided on social networking services. We observed the following hidden states that represent the variable latent states of users, such as enthusiasm for the main contents of the service, playing basic content, and daily routines that are well observed by visiting the service: e.g. receiving login bonuses. Also, we classified the sessions by the latent states of users, such as light user sessions, low motivation sessions, and sessions in which users seem addicted to the main contents.