Category mining by heterogeneous data fusion using PdLSI model in a retail service

Tsukasa Ishigaki, Takeshi Takenaka, Yoichi Motomura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 10th IEEE International Conference on Data Mining, ICDM 2010
Pages857-862
Number of pages6
DOIs
Publication statusPublished - 2010 Dec 1
Externally publishedYes
Event10th IEEE International Conference on Data Mining, ICDM 2010 - Sydney, NSW, Australia
Duration: 2010 Dec 142010 Dec 17

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other10th IEEE International Conference on Data Mining, ICDM 2010
CountryAustralia
CitySydney, NSW
Period10/12/1410/12/17

Keywords

  • Bayesian network
  • Heterogeneous data fusion
  • Large-scale ID-POS data
  • Service engineering
  • Topic model

ASJC Scopus subject areas

  • Engineering(all)

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