Developing transferable clickstream analytic models using sequential pattern evaluation indices

Abstract

In this paper, a method for constructing transferable "web" and "clickstream" prediction models based on sequential pattern evaluation indices is proposed. To predict end points, click streams are assumed as sequential data. Further, a sequential pattern generation method is applied to extract features of each click stream data. Based on these features, a classification learning algorithm is applied to construct click stream end point prediction models. In this study, the evaluation indices for sequential patterns are introduced to abstract each clickstream data for transferring the constructed predictive models between different periods. This method is applied to a benchmark clickstream dataset to predict the end points. The results show that the method can obtain more accurate predictive models with a decision tree learner and a classification rule learner. Subsequently, the evaluation of the availability for transferring the predictive morels between different periods is discussed. © Springer International Publishing 2013.