Skip to main content
SpringerLink
Log in

Periodicity analysis using weighted sequential pattern in recommending service

  • Published:
Cluster Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Banicescu, I., Velusamy, V., Devaprasad, J.: On the scalability of dynamic scheduling scientific applications with adaptive weighted factoring. Clust. Comput. 6(3), 215–226 (2003)

    Article  Google Scholar 

  2. Cho, Y.-S., Ryu, K.-H.: Personalized recommendation system using FP-tree mining based on RFM. J. Korea Soc. Comput. Inf. 17(2), 197–206 (2012)

    Article  Google Scholar 

  3. Zhong, A., et al.: Performance implications of non-uniform VCPU-PCPU mapping in virtualization environment. Clust. Comput. 16(3), 347–358 (2013)

    Article  Google Scholar 

  4. Cho, Y.S., et al.: A study of recommending service based on customer inclination using AHP. In: Proceedings of the 2016 IEEE International Conference on Management of Innovation and Technology (ICMIT), IEEE, pp. 145–149. (2016)

  5. Cho, Y.S., et al.: Clustering method using item preference based on RFM for recommendation system in u-commerce. In: Han, Y.-H. (ed.) Ubiquitous information technologies and applications, pp. 353–362. Springer, Dordrecht (2013)

    Chapter  Google Scholar 

  6. Riha, L., Malik, M., El-Ghazawi, T.: An adaptive hybrid OLAP architecture with optimized memory access patterns. Clust. Comput. 16(4), 663–677 (2013)

    Article  Google Scholar 

  7. Gunaratne, K.A.: Globalisation, decimation of labour and strategic managerial options. In: Twelfth Australia and New Zealand Academy of Management International Conference. (1998)

  8. Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the Eleventh International Conference on Data Engineering, IEEE, pp. 3–14. (1995)

  9. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Acm Sigmod Record, ACM, pp. 207–216. (1993)

    Article  Google Scholar 

  10. Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: International Conference on Extending Database Technology. Springer, Berlin, Heidelberg, pp. 1–17. (1996)

    Google Scholar 

  11. Chiu, D.-Y., Wu, Y.-H., Chen, A.L.P.: An efficient algorithm for mining frequent sequences by a new strategy without support counting. In: Proceedings. 20th International Conference on Data Engineering, 2004, IEEE, p. 375. (2004)

  12. Han, J., et al.: Prefixspan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of the 17th international conference on data engineering. pp. 215–224. (2001)

  13. Yan, X., Han, J., Afshar, R.: CloSpan: mining: closed sequential patterns in large datasets. In: Proceedings of the 2003 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics. pp. 166–177 (2003)

  14. Tibshirani, R., Friedman, J.: The elements of statistical learning: data mining, inference, and prediction. Springer, New York (2001)

    MATH  Google Scholar 

  15. Masseglia, F., Poncelet, P., Teisseire, M.: Incremental mining of sequential patterns in large databases. Data Knowl. Eng. 46(1), 97–121 (2003)

    Article  Google Scholar 

  16. Cheung, D.W., et al.: Maintenance of discovered association rules in large databases: an incremental updating technique. In: Proceedings of the Twelfth International Conference on Data Engineering, 1996. IEEE, pp. 106–114. (1996)

  17. Pudi, V.: Haritsa JR quantifying the utility of the past in mining large databases. Inf. Syst. 25(5), 323–343 (2000)

    Article  Google Scholar 

  18. Wang, K.: Discovering patterns from large and dynamic sequential data. J. Intell. Inf. Syst. 9(1), 33–56 (1997)

    Article  Google Scholar 

  19. Dafa-Alla, A.F.A., et al.: IMTAR: incremental mining of general temporal association rules. JIPS 6(2), 163–176 (2010)

    Google Scholar 

  20. Herlocker, J.L., et al.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, pp. 230–237. (1999)

Download references

Acknowledgements

Funding for this paper was provided by Namseoul University.

Author information

Authors and Affiliations

  1. Department of Computer Science, Database and Bioinformatics Lab., Chungbuk National University, Cheongju, Chungbuk, 28644, Korea

    Young Sung Cho

  2. Department of Computer Science, Namseoul University, Cheonan, Chungnam, 31020, Korea

    Won Shik Na & Song Chul Moon

Authors
  1. Young Sung Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Won Shik Na
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Song Chul Moon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Song Chul Moon.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2871-y

Keywords

Search

Navigation