Periodicity analysis using weighted sequential pattern in recommending service
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Nowadays, due to escalating the demands of the direct purchase of e-commerce with many kinds of item, the demand for e-commerce is exploding. The recommender system finds items for customer easily and targets in customers for the e-commerce firms easily by an automated recommending process. And also, association rules are often used to facilitate product sales in marketing pattern analysis through recommender systems in e-commerce. This paper takes aim at a new recommending service in e-commerce using periodicity analysis of weighted sequential pattern. In e-commerce, we need the FRAT segmentation method based on the various items purchased by the customer, to reflect the weights and perform the pre-processing tasks using sequential patterns for period analysis. We apply an effective incremental sequential data mining method that adds incremental purchasing data as a change of the four season changes. As a result, we suggest a recommendation service for micro-marketing of e-commerce based on big data analysis for promoting e-commerce purchasing, which satisfies customer’s taste with various products of e-commerce. To check the performance of the proposal, we tested the data set with the same conditions as before. As a result, the proposed system is more efficient than the other systems in the results of system evaluation.
KeywordsSegmentation FRAT Association rules Sequential pattern mining
Funding for this paper was provided by Namseoul University.
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