Automatic User Categorization Through Large Transaction Data

摘要

Nowadays, transaction data of users on the network platform has become important raw materials to analyze their purchase behaviors and consumption patterns. In fields such as advertising and marketing, it is important to identify certain groups of users to target. Therefore, an automatic user categorization method is meaningful. In this paper, we introduce a user categorization method on e-commerce transaction data based on the products that users are interested in. The categorization method introduces a novel distance metric for two users and an efficient clustering algorithm based on it. We conduct our experiments on the yelp website, and the dataset with 1.2 million users are tested. Compared with two commonly used clustering algorithms, the proposed method shows the best efficiency, which is suitable for large data sets. Furthermore, the clusters of users obtained are summarized by keywords which are extracted from their review comments.