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Research on e-Commerce User Behavior Analysis Based on Big Data Collaborative Recommendation Algorithm

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DOI: 10.23977/cpcs.2021.51006 | Downloads: 17 | Views: 1251

Author(s)

Xuecong Cao 1, Sisi Chen 1, Zhaoming Li 1

Affiliation(s)

1 State Grid Huitongjincai(Beijing) Information Technology Co., Ltd., Beijing, China

Corresponding Author

Zhaoming Li

ABSTRACT

With the rapid development of Internet, users tend to purchase their favorite products through Internet transactions and online payment. The general trend of e-commerce development in China is that physical trading places are gradually replaced by online trading platforms on the Internet. In this paper, the restricted Boltzmann machine based on category conditions is used to describe the user's own interest preference by using the objective label of the project itself. In this process, only the project information that the user has scored is used, which strengthens the user's personalized needs. The method fully mines user behavior information, replaces commodity content big data with user behavior information as a recommended data set, and can actively push commodity content that users may be interested in to users. Experimental results show that the accuracy of RBM ( Restricted Boltzmann machine) model with nearest neighbor is higher than that of the original model, and the anti-over-fitting ability of the model is also improved.

KEYWORDS

Big data, Collaborative recommendation algorithm, Restricted boltzmann machine: e-commerce, User behavior

CITE THIS PAPER

Xuecong Cao, Sisi Chen, Zhaoming Li, Research on e-Commerce User Behavior Analysis Based on Big Data Collaborative Recommendation Algorithm. Computing, Performance and Communication Systems (2021) Vol. 5: 30-37. DOI: http://dx.doi.org/10.23977/cpcs.2021.51006

REFERENCES

[1] Shuang F, Chen C L P. A Fuzzy Restricted Boltzmann Machine: Novel Learning Algorithms Based on the Crisp Possibilistic Mean Value of Fuzzy Numbers[J]. IEEE Transactions on Fuzzy Systems, 2018, 26no. 1, pp. 117-130.
[2] Chang Hao, Yang Shengquan. (2020). Research on commodity recommendation algorithm based on collaborative filtering decision tree. Value Engineering, vol. 039, no. 009, pp. 127-129.
[3] Shen Weijie, Bian Longjiang, Zhang Xingjian, et al. (2019). Analysis and evaluation of quality information based on big data technology and application research of e-commerce procurement quality control strategy. Modern Management, vol. 9, no. 5, pp. 6.
[4] Wang Ye, Guo Lingli, Song Wenchao, et al. (2018). Research on equipment portrait recommendation algorithm in expert knowledge base based on big data technology. Computer Measurement and Control, vol. 26, no. 12, pp. 225-229.
[5] Li Xiaoying, Zhao Anna, Zhou Xiaojing, et al. (2019). Research and application of personalized product recommendation based on big data analysis and mining platform. Electronic Testing, no. 12, pp. 65-66.
[6] Bian Yuning, Li Yeli, Zeng Qingtao, Sun Yanxiong. (2020). Research on the Application of Improved Collaborative Filtering Recommendation Algorithm in Precision Marketing. Journal of Beijing Institute of Printing, vol. 28, no. 10, pp. 140-145.
[7] A Z J, B N C A, B D B P A, et al. A highly parameterizable framework for Conditional Restricted Boltzmann Machine based workloads accelerated with FPGAs and OpenCL[J]. Future Generation Computer Systems, 2020, 104:201-211.
[8] Liu Yan, Lei Jue. (2020). Research on ship information recommendation model based on collaborative filtering algorithm in big data environment. Ship Science and Technology, vol. 42, no. 04, pp. 155-157.
[9] Yu Tao. (2018). Research on Key Technologies of E-commerce Based on Big Data. Science and Technology Information, vol. 016, no. 036, pp. 35-36.
[10] Chopra P, Yadav S K. (2018). Restricted Boltzmann machine and softmax regression for fault detection and classification[J]. Complex & Intelligent Systems, vol. 4, no. 1, pp. 67-77.

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