E-commerce Webpage Recommendation Scheme Base on Semantic Mining and Neural Networks
DOI:
https://doi.org/10.53469/jtpes.2024.04(03).20Keywords:
E-commerce, webpage recommendation, semantic web mining, BP neural networkAbstract
In e-commerce websites, web mining web page recommendation technology has been widely used. However, recommendation solutions often cannot meet the actual application needs of online shopping users. To address this problem, this paper proposes an e-commerce web page recommendation solution that combines semantic web mining and BP neural networks. First, the web logs of user searches are processed and 5 features are extracted: content priority, time consumption priority, online shopping users' explicit/implicit feedback on the website, recommendation semantics and input deviation amount. Then, these features are used as input features of the BP neural network to classify and identify the priority of the final output web page. Finally, the web pages are sorted according to priority and recommended to users. This project uses book sales webpages as samples for experiments. The results show that this solution can quickly and accurately identify the webpages required by users.
References
Carmona, C. J., Ramírez-Gallego, S., Torres, F., Bernal, E., del Jesus, M. J., & García, S. (2012). Web usage mining to improve the design of an e-commerce website: OrOliveSur. com. Expert Systems with Applications, 39(12), 11243-11249.
Khodabandehlou, S. (2019). Designing an e-commerce recommender system based on collaborative filtering using a data mining approach. International Journal of Business Information Systems, 31(4), 455-478.
Siddiqui, A. T., & Aljahdali, S. (2013). Web mining techniques in e-commerce applications. arXiv preprint arXiv:1311.7388.
Karthik, M., & Swathi, S. (2013). Secure web mining framework for e-commerce websites. International Journal of Computer Trends & Technology, 4(5), 321-334.
Poggi, N., Muthusamy, V., Carrera, D., & Khalaf, R. (2013). Business process mining from e-commerce web logs. In Business Process Management: 11th International Conference, BPM 2013, Beijing, China, August 26-30, 2013. Proceedings (pp. 65-80). Springer Berlin Heidelberg.
Li, Y., Wang, W., Yan, X., Gao, M., & Xiao, M. (2024). Research on the Application of Semantic Network in Disease Diagnosis Prompts Based on Medical Corpus. International Journal of Innovative Research in Computer Science & Technology, 12(2), 1-9. https://doi.org/10.55524/ijircst.2024.12.2.1
Panda, A. K., Sahu, D. K., Dehuri, S. N., & Patra, M. R. WEB MINING: DOCUMENT FILTERING IN E-COMMERCE USING CLUSTERING.
Goh, A. T. (1995). Back-propagation neural networks for modeling complex systems. Artificial intelligence in engineering, 9(3), 143-151.
Guo-yong, L. I., Fang, Y. A. N., & Xiao-feng, G. U. O. (2013). Gray neural network algorithm improved by genetic algorithm. Control Engineering of China, 20(5), 934.
Asjana, M., Batista, V.F., Vicente, M.D., & García, M.N. (2012). Semantic Web Mining for Book Recommendation. International Symposium in Management Intelligent Systems.
Jun-Zhong, J. I., Zhang, L. L., Chen-Sheng, W. U., & Jin-Yuan, W. U. (2014). Semantic weight-based naive Bayesian algorithm for text sentiment classification. Journal of Beijing University of Technology, 40(12), 1884-1890.
Sneha, Y. S., Mahadevan, G., & Prakash, M. (2012). A personalized product based recommendation system using web usage mining and semantic web. International Journal of Computer Theory and Engineering, 4(2), 202.
Inbarani, H., & Thangavel, K. (2013). Web Usage Mining Approaches for Web Page Recommendation: A Survey. In Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods (pp. 271-288). IGI Global.
Yan, X., Xiao, M., Wang, W., Li, Y., & Zhang, F. (2024). A Self-Guided Deep Learning Technique for MRI Image Noise Reduction. Journal of Theory and Practice of Engineering Science, 4(01), 109-117. https://doi.org/10.53469/jtpes.2024.04(01).15
Ma, D., Dang, B., Li, S., Zang, H., & Dong, X. (2023). Implementation of computer vision technology based on artificial intelligence for medical image analysis. International Journal of Computer Science and Information Technology, 1(1), 69-76.
Chen, Y., Liu, C., Huang, W., Cheng, S., Arcucci, R., & Xiong, Z. (2023). Generative text-guided 3d vision-language pretraining for unified medical image segmentation. arXiv preprint arXiv:2306.04811.
Weimin, W. A. N. G., Yufeng, L. I., Xu, Y. A. N., Mingxuan, X. I. A. O., & Min, G. A. O. (2024). Enhancing Liver Segmentation: A Deep Learning Approach with EAS Feature Extraction and Multi-Scale Fusion. International Journal of Innovative Research in Computer Science & Technology, 12(1), 26-34. https://doi.org/10.55524/ijircst.2024.12.1.6
Liao, J., Kot, A., Guha, T., & Sanchez, V. (2020, October). Attention selective network for face synthesis and pose-invariant face recognition. In 2020 IEEE International Conference on Image Processing (ICIP) (pp. 748-752). IEEE.
Dong, X., Dang, B., Zang, H., Li, S., & Ma, D. (2024). The prediction trend of enterprise financial risk based on machine learning arima model. Journal of Theory and Practice of Engineering Science, 4(01), 65-71.
Chen, S., Li, K., Fu, H., Wu, Y. C., & Huang, Y. (2023). Sea ice extent prediction with machine learning methods and subregional analysis in the Arctic. Atmosphere, 14(6), 1023.
Chen, Y., Huang, W., Zhou, S., Chen, Q., & Xiong, Z. (2023). Self-supervised neuron segmentation with multi-agent reinforcement learning. arXiv preprint arXiv:2310.04148.
Chen, Y., Huang, W., Liu, X., Chen, Q., & Xiong, Z. (2023). Learning multiscale consistency for self-supervised electron microscopy instance segmentation. arXiv preprint arXiv:2308.09917.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Wenchao Zhao, Xiaoyi Liu, Ruilin Xu, Lingxi Xiao, Muqing Li
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
