Journal Articles (All Issues)

Abstract

This study presents an AI-based big data classification method to solve the issues of poor recursion efficiency and excessive redundancy in data classification, while taking into consideration the context of modern e-commerce big data. Adopting the lightning-fast Spark architecture, the method sets a vertical sequence governed by the data jurisdiction dimension, greatly improving data mining efficiency. We use data from e-commerce websites to simulate and validate the suggested method. These results demonstrate that the algorithm is both fast and accurate when it comes to data categorization.

References

1. Zhao Qing. Research on Personalized Recommendation Algorithm of chemical product e-commerce based on genetic fuzzy clustering [J]. Bonding, 2020, 44 (11): 4 2. Zhu Zhihui, Zhu Meifang. Research on e-commerce personalized recommendation algorithm based on genetic fuzzy clustering [J]. Journal of Jiujiang University: Natural Science Edition, 2019, 034 (001): 61-65 . 3. Li Qingxia, Wei Wenhong, Cai Zhaoquan. E-commerce personalized recommendation algorithm for hybrid user and project collaborative filtering [J]. Journal of Sun Yat sen University: Natural Science Edition, 2016, 55 (5): 6 4. Zhang Kaisheng, song Wenwei, Li Huizhen. Parallel recommendation algorithm based on fuzzy clustering [J]. Journal of Shaanxi University of Technology: Natural Science Edition, 2019 (4): 57-61 5. Zhao Hua, Lin Zheng, Fang AI, et al. A recommendation algorithm based on knowledge tree and its application in mobile e-commerce [J]. 2021 (2011-6): 54-58 6. Ji Xiaoyan, Li Yulong. Personalized recommendation algorithm based on improved clustering in Hadoop environment [J]. Journal of Lanzhou Jiaotong University, 2017, 036 (001): 70-76 7. Du Xi Xi, Liu Huafeng, Jing Liping. A superposition joint clustering recommendation model integrating social networks [J]. Journal of Shandong University: Engineering Edition, 2018, 48 (3): 7 8. Zhang Yinghui, Li Xue. Tourism recommendation algorithm based on fuzzy clustering [J]. Computer technology and development, 2016, 026 (012): 99-102 9. Gao Tianying, Zhang Zhigang, Li Guoyan, et al. A collaborative filtering recommendation algorithm based on user attribute and website type clustering [J]. Journal of Tianjin urban construction university, 2018, 24 (1): 6 10. Wang Xi, Wang Yanming, Wang, et al. An improved collaborative filtering recommendation algorithm [J]. Modern computer (Professional Edition), 2017, 14 (14): 10-15. 11. Dong Hui, Fang Xiao, Ma Jian, et al. Mobile e-commerce user item clustering collaborative filtering recommendation algorithm based on situational awareness [J]. Journal of Guangxi University for Nationalities (NATURAL SCIENCE EDITION), 2018, 24 (02): 67-74. 12. Shi Yingying, Ge Wancheng, Wang Liangyou, et al. Research on improvement of K-means clustering personalized recommendation algorithm [J]. Information and communication, 2016, No. 157 (01): 19-21. 13. Sun Kele, Deng Xianrong. An improved o2o e-commerce recommendation model based on gradient lifting regression algorithm [J]. Journal of Anhui University of architecture and Architecture: Natural Science Edition, 2016 . 14. Zhang Yinghui, Li Xue. Tourism recommendation algorithm based on fuzzy clustering [J]. Computer technology and development, 2016 (12): 99-102. 15. Wang Min, Ji Shaochun. Research on personalized recommendation of Digital Library Based on fuzzy clustering and fuzzy pattern recognition [J]. Modern information, 2016, 36 (04): 52-56. 16. Wang Xiaojun. Distributed hybrid collaborative filtering method in recommendation system [J]. Journal of Beijing University of Posts and telecommunications, 2016, 39 (002): 25-29. 17. Zhao Yan, Wang Yamin, Liu huailiang. Research on personalized Resource Recommendation Model Based on tag network clustering [J]. 2021 (2014-4): 179-183. 18. Wang Zhaokai, Li Yaxing, Feng Xupeng, et al. Personalized information recommendation based on deep belief network [J]. Computer Engineering, 2016, 42 (010): 201-206. 19. Hu Chaoju, sun Keni. Research on Personalized Recommendation Based on user fuzzy clustering [J]. Software guide, 2018, 017 (002): 31-34. 20. Li Haoyang, Fu Yunqing. Collaborative filtering recommendation algorithm based on tag clustering and project topic [J]. Computer science, 2018, v.45 (04): 247-251. 21. Wang Xiaojun, Fu Chao. Using fuzzy blocking to improve the scalability and accuracy of collaborative filtering [J]. Journal of Beijing University of Posts and telecommunications, 2017 (01): 78-82. 22. Zhang Yanju, Lu Chang. IFCM slope one collaborative filtering recommendation algorithm under missing data [J]. 2021 (2020-9): 185-188 23. 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. 24. 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. 25. Liu X. An improved clustering-based collaborative filtering recommendation algorithm. Cluster Computing, 2017, 20(2):1281-1288. 26. Zheng G, Yu H, Xu W. Collaborative Filtering Recommendation Algorithm with Item Label Features. International Core Journal of Engineering, 2020, 6(1):160-170. 27. 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. 28. 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. 29. 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. 30. 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. 31. 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. 32. Fang X, Wang J, Sheng D, et al. Recommendation algorithm combining ratings and comments. AEJ - Alexandria Engineering Journal, 2021, 60(6):5009-5018. 33. 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. 34. 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 35. Tan Libin, Tang dunbing, Chen Weifang, et al. Open design decision-making method with large-scale user participation [J]. Computer integrated manufacturing system, 2020, 26 (4): 9. 36. Mu Jun. community network data crawler algorithm based on association rule mining [J]. Microelectronics and computer, 2018, 035 (008): 105-108. 37. Liu Jingping, Li Ping. A fuzzy cognitive collaborative filtering algorithm [J]. Computer engineering and science, 2018, 040 (005): 898-905. 38. Zhou Chaojin, Wang Yuzhen. Research on personalized recommendation of agricultural products based on improved collaborative filtering algorithm [J]. Journal of Shaoyang University (NATURAL SCIENCE EDITION), 2017, 014 (006): 23-31 39. KK Rakesh, AS Aneeshkumar, Optimization of Fuzzy Logic-Based Genetic Algorithm Techniques in Wireless Sensor Networks Protocols, International Journal of Intelligent Systems and Applications in Engineering, vol-12, issue-14s, 548-556. 40. Sreela Sreedhar, Varghese Paul, AS Aneeshkumar, Solitude Conserve Attribute Cryptographic CP-ABFE Data Protocols in Fuzzy Cloud Service Provider, Indian Journal of Science and Technology, vol-8, issue-25, 1-5. 41. A.S. Aneeshkumar., C. Jothi Venkateswaran, A novel approach for Liver disorder Classification using Data Mining Techniques, Engineering and Scientific International Journal, vol-2, issue-1, 15-18.