Design and Implementation of Personalized Recommendation Engine for CRM System
DOI:
https://doi.org/10.53469/wjimt.2025.08(08).06Keywords:
Personalized recommendation engine, Machine learning, CRM system, User satisfaction, Sales conversion rateAbstract
This study aims to design and implement a personalized recommendation engine for a CRM system by utilizing machine learning algorithms. This engine aims to provide highly customized product or service recommendations by analyzing user behavior data, in order to improve user satisfaction and sales conversion rates. This article will discuss in detail the selection and implementation process of recommendation algorithms, as well as how to integrate these algorithms into existing CRM systems. We demonstrated the application effectiveness of the recommendation engine in a real business environment through a practical case study. The experimental results show that personalized recommendation engines significantly improve the interaction quality between users and the system, and effectively enhance the overall performance of the business.
References
Peng, Q., Planche, B., Gao, Z., Zheng, M., Choudhuri, A., Chen, T., Chen, C. and Wu, Z., 3D Vision-Language Gaussian Splatting. In The Thirteenth International Conference on Learning Representations.
Pinyoanuntapong, Ekkasit, et al. "Gaitsada: Self-aligned domain adaptation for mmwave gait recognition." 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS). IEEE, 2023.
Zheng, Ce, et al. "Diffmesh: A motion-aware diffusion framework for human mesh recovery from videos." 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025.
Wang, Z., Chew, J. J., Wei, X., Hu, K., Yi, S., & Yi, S. (2025). An Empirical Study on the Design and Optimization of an AI-Enhanced Intelligent Financial Risk Control System in the Context of Multinational Supply Chains. Journal of Theory and Practice in Economics and Management, 2(2), 49–62. Retrieved from https://woodyinternational.com/index.php/jtpem/article/view/208
Liu, Jun, et al. "Toward adaptive large language models structured pruning via hybrid-grained weight importance assessment." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39. No. 18. 2025.
Zhou, Dianyi. "Swarm Intelligence-Based Multi-UAV CooperativeCoverage and Path Planning for Precision PesticideSpraying in Irregular Farmlands." (2025).
Q. Tian, D. Zou, Y. Han and X. Li, "A Business Intelligence Innovative Approach to Ad Recall: Cross-Attention Multi-Task Learning for Digital Advertising," 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), Shenzhen, China, 2025, pp. 1249-1253, doi: 10.1109/AINIT65432.2025.11035473.
Wang, Zhiyuan, et al. "An Empirical Study on the Design and Optimization of an AI-Enhanced Intelligent Financial Risk Control System in the Context of Multinational Supply Chains." (2025).
Xie, Y., Li, Z., Yin, Y., Wei, Z., Xu, G., & Luo, Y. (2024). Advancing Legal Citation Text Classification A Conv1D-Based Approach for Multi-Class Classification. Journal of Theory and Practice of Engineering Science, 4(02), 15–22. https://doi.org/10.53469/jtpes.2024.04(02).03
Chen, Yinda, et al. "Generative text-guided 3d vision-language pretraining for unified medical image segmentation." arXiv preprint arXiv:2306.04811 (2023).
Wu, Xiaomin, et al. "Jump-GRS: a multi-phase approach to structured pruning of neural networks for neural decoding." Journal of neural engineering 20.4 (2023): 046020.
Miao, Junfeng, et al. "Secure and Efficient Authentication Protocol for Supply Chain Systems in Artificial Intelligence-based Internet of Things." IEEE Internet of Things Journal (2025).