Design and implementation of an intelligent recommendation system for product information on an e-commerce platform based on machine learning

The rapid growth of e-commerce platforms has resulted in an overwhelming abundance of product information, leading to choice paralysis and information overload for users. To address these challenges and enhance the user experience, an intelligent recommendation system based on machine learning is proposed. This paper aims to design and implement such a system for e-commerce platform product information. Firstly, we review the relevant theories and technologies of recommendation systems, including the application of Kafka message queues and the Lambda architecture, as well as two common recommendation system types: content-based recommendation and collaborative filtering. Then, we propose a Bayesian pairwise recommendation algorithm based on one-class collaborative filtering and Bayesian pairwise ranking and optimize the model. The performance and effectiveness of the algorithm are validated through experimental evaluation and comparison with other benchmark algorithms. Regarding recommendation system design, we propose the corresponding architecture and modules. Importantly, we conduct functional and non-functional testing to verify the usability and performance of the system. Through this research, we provide a design and implementation solution for an intelligent recommendation system for e-commerce platforms, offering references for improving the accuracy of product recommendations and user experience. Future research can continue optimizing the algorithms and expanding the recommendation system's functionality and scope.