Research on customer classification in E-supermarket by using modified fuzzy neural networks

Yu An Tan; Zuo Wang; Qi Luo

doi:10.1007/978-3-540-72393-6_37

Research on customer classification in E-supermarket by using modified fuzzy neural networks

Yu An Tan^*, Zuo Wang, Qi Luo

^*Corresponding author for this work

School of Cyberspace Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

With the development of network technology and E-commerce, more and more enterprises have accepted the management pattern of E-commerce. In order to meet the personalized needs of customers in E-supermarket, customer classification based on their interests is a key technology for developing personalized E-commerce. Therefore, it is highly needed to have a personalized system for extracting customer features effectively, and analyzing customer interests. In this paper, we proposed a new method based on the modified fuzzy neural network to group the customers dynamically according to their Web access patterns. The results suggest that this clustering algorithm is effective and efficacious. Taking one with another, this new proposed approach is a practical solution to make more visitors become to customers, improve the loyalty degree of customer, and strengthen cross sale ability of websites in E-commerce.

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings
Publisher	Springer Verlag
Pages	301-306
Number of pages	6
Edition	PART 2
ISBN (Print)	9783540723929
DOIs	https://doi.org/10.1007/978-3-540-72393-6_37
Publication status	Published - 2007
Event	4th International Symposium on Neural Networks, ISNN 2007 - Nanjing, China Duration: 3 Jun 2007 → 7 Jun 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	4492 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Symposium on Neural Networks, ISNN 2007
Country/Territory	China
City	Nanjing
Period	3/06/07 → 7/06/07

Keywords

Customer classification
E-supermarket
Modified fuzzy neural networks
Personalized needs
Web access

Access to Document

10.1007/978-3-540-72393-6_37

Cite this

Tan, Y. A., Wang, Z., & Luo, Q. (2007). Research on customer classification in E-supermarket by using modified fuzzy neural networks. In Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings (PART 2 ed., pp. 301-306). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4492 LNCS, No. PART 2). Springer Verlag. https://doi.org/10.1007/978-3-540-72393-6_37

Tan, Yu An ; Wang, Zuo ; Luo, Qi. / Research on customer classification in E-supermarket by using modified fuzzy neural networks. Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings. PART 2. ed. Springer Verlag, 2007. pp. 301-306 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "Research on customer classification in E-supermarket by using modified fuzzy neural networks",

abstract = "With the development of network technology and E-commerce, more and more enterprises have accepted the management pattern of E-commerce. In order to meet the personalized needs of customers in E-supermarket, customer classification based on their interests is a key technology for developing personalized E-commerce. Therefore, it is highly needed to have a personalized system for extracting customer features effectively, and analyzing customer interests. In this paper, we proposed a new method based on the modified fuzzy neural network to group the customers dynamically according to their Web access patterns. The results suggest that this clustering algorithm is effective and efficacious. Taking one with another, this new proposed approach is a practical solution to make more visitors become to customers, improve the loyalty degree of customer, and strengthen cross sale ability of websites in E-commerce.",

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Tan, YA, Wang, Z & Luo, Q 2007, Research on customer classification in E-supermarket by using modified fuzzy neural networks. in Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 4492 LNCS, Springer Verlag, pp. 301-306, 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, 3/06/07. https://doi.org/10.1007/978-3-540-72393-6_37

Research on customer classification in E-supermarket by using modified fuzzy neural networks. / Tan, Yu An; Wang, Zuo; Luo, Qi.
Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings. PART 2. ed. Springer Verlag, 2007. p. 301-306 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4492 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Research on customer classification in E-supermarket by using modified fuzzy neural networks

AU - Tan, Yu An

AU - Wang, Zuo

AU - Luo, Qi

PY - 2007

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N2 - With the development of network technology and E-commerce, more and more enterprises have accepted the management pattern of E-commerce. In order to meet the personalized needs of customers in E-supermarket, customer classification based on their interests is a key technology for developing personalized E-commerce. Therefore, it is highly needed to have a personalized system for extracting customer features effectively, and analyzing customer interests. In this paper, we proposed a new method based on the modified fuzzy neural network to group the customers dynamically according to their Web access patterns. The results suggest that this clustering algorithm is effective and efficacious. Taking one with another, this new proposed approach is a practical solution to make more visitors become to customers, improve the loyalty degree of customer, and strengthen cross sale ability of websites in E-commerce.

AB - With the development of network technology and E-commerce, more and more enterprises have accepted the management pattern of E-commerce. In order to meet the personalized needs of customers in E-supermarket, customer classification based on their interests is a key technology for developing personalized E-commerce. Therefore, it is highly needed to have a personalized system for extracting customer features effectively, and analyzing customer interests. In this paper, we proposed a new method based on the modified fuzzy neural network to group the customers dynamically according to their Web access patterns. The results suggest that this clustering algorithm is effective and efficacious. Taking one with another, this new proposed approach is a practical solution to make more visitors become to customers, improve the loyalty degree of customer, and strengthen cross sale ability of websites in E-commerce.

KW - Customer classification

KW - E-supermarket

KW - Modified fuzzy neural networks

KW - Personalized needs

KW - Web access

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Tan YA, Wang Z, Luo Q. Research on customer classification in E-supermarket by using modified fuzzy neural networks. In Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings. PART 2 ed. Springer Verlag. 2007. p. 301-306. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-540-72393-6_37

Research on customer classification in E-supermarket by using modified fuzzy neural networks

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