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Mobile Networks and Applications

, Volume 24, Issue 1, pp 163–170 | Cite as

An Intelligent Product Recommendation Model to Reflect the Recent Purchasing Patterns of Customers

  • Haein Kim
  • Geunho Yang
  • Hosang Jung
  • Sang Ho Lee
  • Jae Joon AhnEmail author
Article

Abstract

This study suggests a new product recommendation model to reflect the recent purchasing patterns of customers. There are many methods to measure the similarity between customers or products using one-way collaborative filtering. However, few studies have calculated the similarity of using both customer information and product information. Therefore, in this study, affinity variables that combine customer data with product data are created through a confusion matrix. Various derived variables are also generated to enhance the forecasting performance in enormous analysis data. In this study, various data mining classifiers such as the decision tree, neural network, support vector machine, random forest, and rotation forest are applied, and a sliding-window scheme is considered to construct the recommendation model.

Keywords

Product recommendation model Purchasing patterns of customers Affinity Confusion matrix Data mining classifier Decision tree 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Haein Kim
    • 1
  • Geunho Yang
    • 1
  • Hosang Jung
    • 2
  • Sang Ho Lee
    • 3
  • Jae Joon Ahn
    • 1
    Email author
  1. 1.Department of Information & StatisticsYonsei UniversityWonjuSouth Korea
  2. 2.Asia Pacific School of LogisticsInha UniversityIncheonSouth Korea
  3. 3.Department of Information Technology ManagementSunmoon UniversityAsanSouth Korea

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