Category Mining by Heterogeneous Data Fusion Using PdLSI Model in a Retail Service

Abstract

This paper describes an appropriate category discovery method that simultaneously involves a customer's lifestyle category and item category for the sustainable management of retail services, designated as ``category mining''. Category mining is realized using a large-scale ID-POS data and customer's questionnaire responses with respect to their lifestyle. For the heterogeneous data fusion, we propose a probabilistic double-latent semantic indexing (PdLSI) model that is an extension of PLSI model. In the PdLSI model, customers and items are classified probabilistically into some latent lifestyle categories and latent item category. Then, understanding of relation between the latent categories and various purchased situations is realized using Bayesian network modeling. This method provides useful knowledge based on a large-scale data for efficient customer relationship management and category management, and can be applicable for other service industries.