Predicting purchases with using the variety of customer behaviors: Analysis of the purchase history and the browsing history by deep learning

Junichiro Niimi, Takahiro Hoshino

Research output: Contribution to journalArticle

2 Citations (Scopus)


Nowadays, along with the popularity of E-Commerce, the marketing strategy of retail stores has been more complicated with O2O or Omni-channel. Therefore, Customer Relationship Management (CRM) is one of the important issue for the retail stores. It can be difficult to predict customers future behavior with the simple quantitive information such as purchase frequency since each customers are widely diversified. Although the company can obtain the variety of customers information from their online activity, the use of access history is still limited. In this paper, we defined “the variety of user access patterns” collected from their web browsing history and it shows the patterns they visit the website. Finally, we verified its effectiveness with developing a DNN model to predict customers future behavior.

Original languageEnglish
JournalTransactions of the Japanese Society for Artificial Intelligence
Issue number2
Publication statusPublished - 2017 Jan 1



  • Browsing history
  • Deep learning
  • Electronic commerce
  • Neural network
  • Purchase prediction

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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