PerNN: A Deep Learning-Based Recommendation Algorithm for Personalized Customization

Abstract

In the context of the Internet, the personalization and diversification of customer demands present a significant challenge for research on the identification, combination, and utilization of personalized demand feature elements. A key difficulty lies in achieving real-time perception, processing, and recognition of customer needs to dynamically identify and understand personalized customer intent. To address the limitations, we propose a Personalized customization-based Neural Network (PerNN), designed to enhance the performance and accuracy of recommendation systems in large-scale and complex information environments. The PerNN model introduces a Personalized Features Layer (PF), which effectively integrates multi-dimensional information—including historical interaction data, social network relationships, and users’ temporal behavior patterns—to generate fine-grained, personalized user feature representations. This approach significantly improves the model’s ability to predict user preferences. Extensive experiments conducted on public datasets demonstrate that the PerNN model consistently outperforms existing methods, particularly regarding the accuracy and response speed of personalized recommendations. The results validate the effectiveness and superiority of the proposed model in managing complex and recommendation tasks, offering a novel and efficient solution for personalized customization scenarios.