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Personalized Recommendation Research Based on Logistic Regression Algorithm for Amazon Product Reviews
1
School of Foreign Languages and Cultures, Chongqing University, Chongqing 401331, China
2
School of Public Administration, Sichuan University, Chengdu 610064, China
*
Author to whom correspondence should be addressed.
JIEM 2024 5(1):76; https://doi.org/10.69610/j.iem.202406264
Received: 10 April 2024 / Accepted: 25 June 2024 / Published Online: 26 June 2024
Abstract
This study explores personalized recommendation strategies within Amazon's product review system using the Logistic Regression algorithm. By analyzing user behavior and review data, a predictive model was developed to forecast user preferences for specific products. The research employed extensive real-world data and validated the model's effectiveness and accuracy through empirical analysis. Findings indicate that the proposed personalized recommendation system significantly enhances user experience and increases product sales. The study contributes an effective recommendation algorithm for e-commerce platforms, offering practical implications for enhancing user engagement and optimizing marketing strategies in competitive markets.
Keywords:
Logistic Regression algorithm;
personalized recommendation;
Amazon product reviews;
user behavior analysis;
Copyright: © 2024 by Wang and Qi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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ACS Style
Wang, J.; Qi, Y. Personalized Recommendation Research Based on Logistic Regression Algorithm for Amazon Product Reviews. Journal of Innovations in Economics & Management, 2024, 5, 76. doi:10.69610/j.iem.202406264
AMA Style
Wang J, Qi Y. Personalized Recommendation Research Based on Logistic Regression Algorithm for Amazon Product Reviews. Journal of Innovations in Economics & Management; 2024, 5(1):76. doi:10.69610/j.iem.202406264
Chicago/Turabian Style
Wang, Jinfeng; Qi, Yuzhi 2024. "Personalized Recommendation Research Based on Logistic Regression Algorithm for Amazon Product Reviews" Journal of Innovations in Economics & Management 5, no.1:76. doi:10.69610/j.iem.202406264
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References
- Chen, Q. (2023). Intelligent recommendation method for product information of e-commerce platform based on machine learning algorithm. In *2023 2nd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS)*. https://doi.org/10.1109/AIARS59518.2023.00099
- Chen, X., & He, K. (2021). Exploring simple Siamese representation learning. https://doi.org/10.1109/CVPR46437.2021.01549
- Dey, R., Nanda, I., Islam, M. S., et al. (2022). Product recommendation system using machine learning through big data in e-commerce website. In *International Conference on Power Electronics & Sustainable Development (ICPESD)*. https://doi.org/10.26480/icpesd.03.2022.229.233
- Farooqi, R. A., Kesarwani, S., Shakeeb, M., et al. (2022). Enhancing e-commerce applications with machine learning recommendation systems. *International Journal of Scientific Research in Science, Engineering and Technology*. https://doi.org/10.32628/ijsrset122935
- Gattis, M., Winstanley, A., & Bristow, F. (2022). Parenting beliefs about attunement and structure are related to observed parenting behaviours. https://doi.org/10.1080/23311908.2022.2082675
- Liu, L. (2022). E-commerce personalized recommendation based on machine learning technology. *Mobile Information Systems*. https://doi.org/10.1155/2022/1761579
- McKibbin, W., & Fernando, R. (2021). The global macroeconomic impacts of COVID-19: Seven scenarios. https://doi.org/10.1162/asep_a_00796
- Mykhalchuk, T., Zatonatska, T., Dluhopolskyi, O., et al. (2021). Development of recommendation system in e-commerce using emotional analysis and machine learning methods. In *2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)*. https://doi.org/10.1109/IDAACS53288.2021.9660854
- Page, M. J., McKenzie, J. E., Bossuyt, P. M., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. https://doi.org/10.1371/journal.pmed.1003583
- Peng, N., Xiao, X., Di, W., et al. (2023). Design and implementation of an intelligent recommendation system for product information on an e-commerce platform based on machine learning. https://doi.org/10.1117/12.3013353
- Purcell, W., & Neubauer, T. (2023). Digital twins in agriculture: A state-of-the-art review. https://doi.org/10.1016/j.atech.2022.100094
- Rahman, M. S., Sarkar, T. D., Mitasha, U. T., et al. (2024). E-commerce-based smart recommendation system using element-by-element collaborative filtering following with the machine learning technology. In *2024 International Conference on Communication, Computing and Internet of Things (IC3IoT)*. https://doi.org/10.1109/IC3IoT60841.2024.10550386
- Tran, D., & Huh, J. (2022). New machine learning model based on the time factor for e-commerce recommendation systems. *The Journal of Supercomputing, 79*. https://doi.org/10.1007/s11227-022-04909-2
- Xu, K., Zhou, H., Zheng, H., et al. (2024). Intelligent classification and personalized recommendation of e-commerce products based on machine learning. *ArXiv, 2024*, abs/2403.19345. https://doi.org/10.48550/arXiv.2403.19345
- Zhang, X., Guo, F., Chen, T., et al. (2023). A brief survey of machine learning and deep learning techniques for e-commerce research. *J. Theor. Appl. Electron. Commer. Res., 18*. https://doi.org/10.3390/jtaer18040110