Abstract
Analyzing and predicting the tendency of consumers online shopping is the precondition of providing personalized recommendation service, and has attracted more and more attentions. Most of e-commerce platform shave various types of products, and there exists tremendous difference in consumers’ occupation, education background and other personalized features. This Paper realizes a TOPSIS Method which is based on entropy and fuzzy numbers. Compared to the traditional TOPSIS method, with the Association Rules mining method of data mining, the improved TOPSIS solves the problem in traditional TOPSIS method which requires manual intervention during execution. In this study, to implement intelligent tendency predicting and analysis of consumers online shopping based on data driven, three steps is carried out. Firstly, the data mining method is leveraged to obtain the fuzzy weights of evaluation indicator through analyzing the electric business transaction data, and then a fuzzy decision-making matrix is established between product and consumer’s attribute; finally, a product category sequence which can indicate the tendency of consumer online shopping is established through calculating.
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The 36 times report of Internet development status statistics of china. China Internet Network Information Center, pp. 3–11 (2015)
Harremoes, P., Topsoe, F.: Maximum entropy fundamentals. Entropy 3, 191–226 (2001)
Yang, H., Lin, L., Yu, Z.: A class of fuzzy multiple attributes TOPSIS decision making based on exponential type fuzzy numbers. Comput. Eng. Appl. 48(34), 120–124 (2012)
Wu, C., Fan, X.: Fuzzy entropy based human resource’s structure optimal configuration. Chin. J. Manage. Sci. 10(4), 43–47 (2002)
Li, C-Y., Xu, M.-Q.: The importance analysis of equipment based on the improved TOPSIS. J. Vib. Shock 28(6), 19–27 (2009)
Deng, X., Li, J., Zeng, H., Chen, J.: Research on computation methods of AHP wight vector and its applications. Math. Pract. Theory 42(7), 93–100 (2012)
Abbattista, F., Degemmis, M., Fanizzi, N., et al.: Learning User Profiles for Content-Based Filtering in E-Commerce. SSRN
Zhang, X., Wu, Q.: Research on the personalized recommendation based on TOPSIS method. J. Intell. 12(2), 23–28 (2009)
Xu, Z.: New method for uncertain multi-attribute decision making problems. J. Syst. Eng. 17(2), 176–181 (2002)
Xu, Z., Da, Q.: Possibility degree method for ranking interval numbers and its application. J. Syst. Eng. 18(1), 67–70 (2003)
Acknowledgement
This paper is Acknowledged by the key technologies R&D program project of Hebei province (15210110D), and the key technologies R&D program project of Tangshan (14130233B).
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Ma, Y., Liu, P., Huang, D. (2016). Research on the Tendency of Consumer Online Shopping Based on Improved TOPSIS Method. In: Chen, W., et al. Big Data Technology and Applications. BDTA 2015. Communications in Computer and Information Science, vol 590. Springer, Singapore. https://doi.org/10.1007/978-981-10-0457-5_28
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DOI: https://doi.org/10.1007/978-981-10-0457-5_28
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