Edge Computing Enabled Lightweight Neural Network for User Preference Based Customization Recommendation Mechanism

Abstract

The rapid development of computer and network technologies has led to an explosion of information, ushering in an era of e‐commerce dominated by online shopping. This shift underscores the need for precise, efficient, and customized recommendation systems. Traditional search engines struggle to cater to users' diverse information needs, leading to information overload and “dark information.” The customization recommendation systems emerge as a solution, leveraging user data to discern product correlations and formulate recommendations. However, remote server latency poses a challenge for real‐time recommendations. In this regard, we necessitate the exploration of edge computing and propose an edge computing enabled lightweight neural network for a user preference‐based customization recommendation mechanism including the edge computing based user preference model, the customization preference relevance, and the lightweight neural network‐based customization recommendation. In particular, the user preference model quantifies user affinity toward products and a preference connection pattern that ensures a clean relationship map by mitigating attribute interference, while the customization recommendation integrates back‐propagation for nonlinear input–output mappings, achieving generalization while maintaining efficiency. Experimental results indicate that the proposed mechanism can enhance customization and efficiency in recommendation systems when compared to the state‐of‐the‐art methods in terms of the recommendation accuracy, error rate as well as iterations.