Topic tracking model for analyzing consumer purchase behavior

A new topic model is proposed for tracking timevarying consumer purchase behavior, in which consumer interests and item trends change over time, which means the online nature of the proposed method means it can considerably reduce the computational cost and the memory requirement.

Abstract

We propose a new topic model for tracking timevarying consumer purchase behavior, in which consumer interests and item trends change over time. The proposed model can adaptively track changes in interests and trends based on current purchase logs and previously estimated interests and trends. The online nature of the proposed method means we do not need to store past data for current inferences and so we can considerably reduce the computational cost and the memory requirement. We use real purchase logs to demonstrate the effectiveness of the proposed method in terms of the prediction accuracy of purchase behavior and the computational cost of the inference. 1