Application of back-propagation neural network for e-commerce customerspatterning

Pao Hua Chou, Chao Hsing Hsu, Chi Fei Wu, Pi Hsiang Li, Menq-Jiun Wu

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

This paper intends to build up a data analyzing system by mining customers'inner desires on target products through, their specific knowledge, and, thusto automate all, the process of strategy-forming and, product-promotion of EC.The major objectives of this paper are to deploy a hybrid framework to improveweb mining effectively and efficiently, deploy a sequence mining to analyzeuser's navigation, pattern and provide personalized promoted products for eachspecific individual customer in the future and deploy the application of the newmodel to a real, world business case analysis. Several, techniques are employedby this research,. First. "Footstep graph," is used, to visualize theuser's "click-stream data/'. As a result, any particular pattern can bedetected easily and quickly. Secondly, a novel sequence mining technique isapplied, to identify pre-designated, user's navigation, pattern. Third, theback-propagation network (BPN) models are integrated efficiently at the sametime. The techniques listed above are verified by empirical theory to predictprecisely the user's navigation behavior and to categorize his/her desire.ICIC International

Original languageEnglish
Pages (from-to)775-785
Number of pages11
JournalICIC Express Letters
Volume3
Issue number3
Publication statusPublished - 2009 Sep 1

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Cite this