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

Nianjiao Peng; Xunyong Xiao; Wu Di; Li Ang; Manhong Wen

doi:10.1117/12.3013353

29 November 2023 Design and implementation of an intelligent recommendation system for product information on an e-commerce platform based on machine learning

Nianjiao Peng, Xunyong Xiao, Wu Di, Li Ang, Manhong Wen

Author Affiliations +

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129371K (2023) https://doi.org/10.1117/12.3013353
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

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.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nianjiao Peng, Xunyong Xiao, Wu Di, Li Ang, and Manhong Wen "Design and implementation of an intelligent recommendation system for product information on an e-commerce platform based on machine learning", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129371K (29 November 2023); https://doi.org/10.1117/12.3013353

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KEYWORDS

Data modeling

Intelligence systems

Tunable filters

Education and training

Design and modelling

Feature extraction

Machine learning

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