Periodicity analysis using weighted sequential pattern in recommending service

Abstract

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.

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Acknowledgements

Funding for this paper was provided by Namseoul University.

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Correspondence to Song Chul Moon.

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Cho, Y.S., Na, W.S. & Moon, S.C. Periodicity analysis using weighted sequential pattern in recommending service. Cluster Comput 22, 1049–1056 (2019). https://doi.org/10.1007/s10586-018-2871-y

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Keywords

  • Segmentation
  • FRAT
  • Association rules
  • Sequential pattern mining