A personalized recommendation procedure for Internet shopping support

Jae Kyeong Kim, Yoon Ho Cho, Woo Ju Kim, Je Ran Kim, Ji Hae Suh

Research output: Contribution to journalArticle

98 Citations (Scopus)

Abstract

The rapid growth of e-commerce has caused product overload where the customer is no longer able to effectively choose the products he/she is exposed to. To overcome the product overload of Internet shoppers, several recommender systems have been developed. Recommendation systems track past actions of a group of customers to make a recommendation to individual members of the group. We introduce a personalized recommendation procedure by which we can get further recommendation effectiveness when applied to Internet shopping malls. The suggested procedure is based on Web usage mining, product taxonomy, association rule mining, and decision tree induction. We applied the procedure to a leading Internet shopping mall in Korea for performance evaluation, and some experimental results are provided. The experimental results show that choosing the right level of product taxonomy and the right customers increases the quality of recommendations.

Original languageEnglish
Pages (from-to)301-313
Number of pages13
JournalElectronic Commerce Research and Applications
Volume1
Issue number3-4
DOIs
Publication statusPublished - 2002

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Networks and Communications
  • Marketing
  • Management of Technology and Innovation

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