Evolutionary intelligent agents for e-commerce: Generic preference detection with feature analysis

Sheng Uei Guan*, Tai Kheng Chan, Fangming Zhu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


Product recommendation and preference tracking systems have been adopted extensively in e-commerce businesses. However, the heterogeneity of product attributes results in undesired impediment for an efficient yet personalized e-commerce product brokering. Amid the assortment of product attributes, there are some intrinsic generic attributes having significant relation to a customer's generic preference. This paper proposes a novel approach in the detection of generic product attributes through feature analysis. The objective is to provide an insight to the understanding of customers' generic preference. Furthermore, a genetic algorithm is used to find the suitable feature weight set, hence reducing the rate of misclassification. A prototype has been implemented and the experimental results are promising.

Original languageEnglish
Pages (from-to)377-394
Number of pages18
JournalElectronic Commerce Research and Applications
Issue number4
Publication statusPublished - 2005
Externally publishedYes


  • E-commerce
  • Feature analysis
  • Generic attributes
  • Generic preference
  • Genetic algorithm

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