Abstract
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.
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Jang, E.S., Lee, Y.K. (2006). A Method of Recommending Buying Points for Internet Shopping Malls. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_119
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DOI: https://doi.org/10.1007/11892960_119
Publisher Name: Springer, Berlin, Heidelberg
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