| Autor: |
Cho, Young Sung, Na, Won Shik, Moon, Song Chul |
| Předmět: |
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| Zdroj: |
Cluster Computing; Dec2019, Vol. 22 Issue 4, p1049-1056, 8p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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