A Method of Recommending Buying Points for Internet Shopping Malls

  • Eun Sill Jang
  • Yong Kyu Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)


When a customer wants to buy an item in an Internet shopping mall, one of his/her difficulties would be to decide when to buy the item, because its price changes over time. If the shopping mall can recommend appropriate buying points, this would greatly help the customer. Therefore, in this paper, a method of recommending buying points based on time series analysis is proposed using a database of past item prices. The procedure for providing buying points for an item is as follows. First, the past time series patterns are searched for from the database using normalized similarities, which are similar to the current time series pattern of the item. Second, the retrieved past patterns are analyzed and the item’s future price pattern is predicted. Third, using the future price pattern, a recommendation on when to buy the item is made.


Window Size Time Series Data Selling Price Prediction Rate Recommendation Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Eun Sill Jang
    • 1
  • Yong Kyu Lee
    • 2
  1. 1.Department of Computer EngineeringDongguk University 
  2. 2.Corresponding Author, Department of Computer EngineeringDongguk UniversitySeoulRepublic of Korea

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