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E-commerce Recommendation Algorithm Based on Big Data Analysis and Genetic Fuzzy Clustering

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DOI: 10.23977/ferm.2023.060904 | Downloads: 21 | Views: 401

Author(s)

Jiwang You 1

Affiliation(s)

1 Nanjing University Business School, Nanjing, Jiangsu, China

Corresponding Author

Jiwang You

ABSTRACT

With the continued expansion of the EC scale, personalized recommendation technology is widely used. However, traditional referral systems cannot meet current data processing needs, and the presence of highly powerful big data analytics capabilities is a fundamental prerequisite for new personalized referral systems. The paper focuses primarily on EC recommended algorithm research based on big data analysis and fuzzy clustering gene analysis. Based on the literature data, understand the basic theoretical issues related to EC boosting calculations and analyze the methods of genetic fuzzy group analysis. The EC promotion algorithm is designed and the designed algorithm is tested. In conclusion, the algorithm given in this work has a low MAE like the other two algorithms, so its configuration quality is high.

KEYWORDS

Genetic Algorithm, Fuzzy Clustering, Recommendation Algorithm, EC

CITE THIS PAPER

Jiwang You, E-commerce Recommendation Algorithm Based on Big Data Analysis and Genetic Fuzzy Clustering. Financial Engineering and Risk Management (2023) Vol. 6: 28-33. DOI: http://dx.doi.org/10.23977/ferm.2023.060904.

REFERENCES

[1] Lu Q, Guo F. A novel e-commerce customer continuous purchase recommendation model research based on colony clustering. International Journal of Wireless & Mobile Computing, 2016, 11(4):309-317.
[2] Hu Q Y, Zhao Z L, Wang C D, et al. An Item Orientated Recommendation Algorithm from the Multi-view Perspective. Neurocomputing, 2017, 269(dec. 20):261-272.
[3] Liu X. An improved clustering-based collaborative filtering recommendation algorithm. Cluster Computing, 2017, 20(2):1281-1288.
[4] Zheng G, Yu H, Xu W. Collaborative Filtering Recommendation Algorithm with Item Label Features. International Core Journal of Engineering, 2020, 6(1):160-170.
[5] Cui L, Huang W, Qiao Y, et al. A novel context-aware recommendation algorithm with two-level SVD in social networks. Future Generation Computer Systems, 2017, 86(SEP.):1459-1470.
[6] Feng W, Zhu Q, Zhuang J, et al. An expert recommendation algorithm based on Pearson correlation coefficient and FP-growth. Cluster Computing, 2019, 22(3):1-12.
[7] Zhu H, Tian F, Wu K, et al. A multi-constraint learning path recommendation algorithm based on knowledge map. Knowledge-Based Systems, 2018, 143(MAR.1):102-114.
[8] Yang F, Wang H, Fu J. Improvement of recommendation algorithm based on Collaborative Deep Learning and its Parallelization on Spark. Journal of Parallel and Distributed Computing, 2021, 148(2):58-68.
[9] Zhou X, Su M, Feng G, et al. Intelligent Tourism Recommendation Algorithm based on Text Mining and MP Nerve Cell Model of Multivariate Transportation Modes. IEEE Access, 2020, PP (99):1-1.
[10] Fang X, Wang J, Sheng D, et al. Recommendation algorithm combining ratings and comments. AEJ - Alexandria Engineering Journal, 2021, 60(6):5009-5018.
[11] Akter S, Wamba S F. Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 2016, 26(2):173-194.
[12] Fan Y, Ju J, Xiao M. Reputation premium and reputation management: Evidence from the largest e-commerce platform in China. International Journal of Industrial Organization, 2016, 46(May):63-76.

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