ENHANCING E-COMMERCE COMPETITIVENESS: CNN-BASED PRICE COMPSRISON APPLICATION
DOI:
https://doi.org/10.63458/ijerst.v2i4.95Keywords:
Price Comparison E-Commerce, CNNS, Automated Extraction, Cross-Platform Comparison, Textual Data Processing, Visual Data Processing, Real-Time Updates, User Experience, Shopping Optimization.Abstract
This paper introduces an innovative method for price comparison on e-commerce websites using Convolutional Neural Networks (CNNs). We discuss the importance of price comparison, highlight the flaws in manual methods, and examine the limitations of existing systems. Our CNN-based solution automates price extraction and analysis from various online retailers, streamlining cross-platform comparisons. By processing both textual and visual data, such as product descriptions and images, our approach improves accuracy. Real-time data updates enhance the user experience. Through rigorous testing, we demonstrate the effectiveness of our CNN-based solution, promising a superior shopping experience for e-commerce consumers.
References
Durowoju, O., Chan, H. K., & Wang, X.. Investigation of the effect of e-platform information security breaches: A small and medium enterprise supply chain perspective. IEEE Transactions on Engineering Management, 73(3), 1–16. 2020
Lu, H.. Analysis of economic benefit of e-commerce business in oceangoing enterprises. Journal of Coastal Research, 106(1), 217–223. https://doi.org/10.2112/si106-051.1, 2020
Gao, S., Chen, X., Ren, Z., Zhao, D., & Yan, R., Meaningful answer generation of e-commerce question-answering. ACM Transactions on Information Systems, 39(2), 1–26. https://doi.org/10.1145/3432689,2021
Vinsensius, A., Wang, Y., Chew, E. P., & Lee, L. H., Dynamic incentive mechanism for delivery slot management in e-commerce attended home delivery. Transportation Science, 54, 6–12. 2020 https://doi.org/10.1287/trsc.2019.0953
Gee, I. M., Heard, B. R., Webber, M. E., & Miller, S. A. ,The future of food: Environmental lessons from e-commerce. Environmental Science & Technology, 54(23), 14776–14784. 2020 https://doi.org/10.1021/acs.est.0c01731
Liu, S., Newman, C., Buesching, C. D., & Macdonald, D., E-commerce promotes trade in invasive turtles in China. Oryx, 55, 1–4. https://doi.org/10.1017/s0030605319001030, 2020
Mao, M., Lu, J., Han, J., Weiping, H., & Ningbo, C. ,Multi-objective e-commerce recommendations based on hypergraph ranking. Information Sciences, 471, 267–289. 2019 https://doi.org/10.1016/j.ins.2018.07.029
Zhou, L., Hong, Y., Wang, S., & Ruihua, H. ,Learning-centric wireless resource allocation for edge computing: Algorithm and experiment. IEEE Transactions on Vehicular Technology, 70(1), 1.,2020
Zu, E. H., Shu, M., Huang, J. C., Hsu, B. M., & Hu, C. M., Management problems of modern logistics information systems based on data mining. Mobile Information Systems, 2021, 15242921. 2021
Luo, Y., Yang, Z., Liang, Y., Zhang, X., & Xiao, H., Exploring energy-saving refrigerators through online e-commerce reviews: An augmented mining model based on machine learning methods. Kybernetes, 6. https://doi.org/10.1108/K-11-2020-0788, 2021
Qu, Q., Liu, C., & Bao, X. Z., E-commerce enterprise supply chain financing risk assessment based on linked data mining and edge computing. Mobile Information Systems, 2021, 9. 2021 https://doi.org/10.1155/2021/9938325
Lai, J. F., & Cai, S., Design of Sino-Japanese cross-border e-commerce platform based on FPGA and data mining. Microprocessors and Microsystems, 80, 103360., 2021
Ghavamipoor, H., Hashemi Golpayegani, S. A., & Shahpasand, M., A QoS-sensitive model for e-commerce customer behavior. The Journal of Research in Indian Medicine, 11(4), 380–397. 2017 https://doi.org/10.1108/jrim-08-2016-0080
Luk, C. C., Choy, K. L., & Lam, H. Y, Design of an intelligent customer identification model in the e-commerce logistics industry. Engineering Applications of Artificial Intelligence Conference, 22504003. 2018
Qi, J., Zhang, Z., Jeon, S., & Zhou, Y. ,Mining customer requirements from online reviews: A product improvement perspective. Information & Management, 53(8), 951–963. 2016 https://doi.org/10.1016/j.im.2016.06.002
Vanderveld, A., Pandey, A., Han, A., & Parekh, R.,An engagement-based customer lifetime value system for e-commerce. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016, San Francisco, CA, USA, 293–302. 2016
Mach-Krol, M., & Hadasik, B.,’On a certain research gap in big data mining for customer insights. Applied Sciences - Basel, 11(5), 6993. https://doi.org/10.3390/app11156993, 2021
Peng, X., Li, X., & Yang, X., Analysis of the circular economy of the e-commerce market based on a grey model under the background of big data. Journal of Enterprise Information Management, 1, 15. 2021 https://doi.org/10.1108/jeim-01-2021-0015
Zhao, Y., Zhou, Y., & Deng, W.,’Innovation mode and optimization strategy of B2C e-commerce logistics distribution under big data. Sustainability, 12(8), 3381–3390. https://doi.org/10.3390/su12083381,2020
Zhang, B., Tan, R., & Lin, C. J., Forecasting of e-commerce transaction volume using a hybrid of extreme learning machine and improved moth-flame optimization algorithm. Applied Intelligence, 51(2), 952–965. 2020