When and which products to recommend to whom has been the essential issue for retailers. In this field, the topic model is attracting researchers’ attention for extracting customers’ purchase behavior instead of association rules or K-means. However, the optimal number of topics is chosen manually, and there are some limitations to use topic models. In this study, we developed the model by Koltcov et al. for point of sales (POS) data in the supermarket. To grasp the change of topics over time, we divided five-month POS data into ten datasets into two-week intervals and applied the topic model with Renyi entropy separately. The results suggest that splitting data might be a better way to understand customer’s behavior.
