# Research and Application of Edge Computing and Deep Learning in a Recommender System
## 3 Recommendation Models Based on Knowledge Distillation and Edge Computing  
A traditional deep learning model is typically built via the superposition of multi-layer neural networks. Performance and computational complexity vary among models with different network layers. In addition, significant differences lie in computing resource demands and data sizes at diverse network layers. For deep learning-based recommender systems, achieving robust performance often requires a high-powered computing data center in the cloud. In the present study, knowledge distillation was combined with deep learning and edge computing to reduce resource demands in the computing center, improve data transmission efficiency, and overcome resource limitations of terminal devices. Model compression could be realized with the help of knowledge distillation, reducing memory costs and boosting computing speed. At the same time, the trained model was segmented, and some of these segmented models could be deployed at terminal nodes leveraging the computational advantages of proximity and timeliness in edge computing. As a result, both the model reasoning latency and energy consumption were reduced.  
In order to reduce the complexity of the model and improve accuracy, two important steps are proposed in this research:  
1. In theory, the search space of the model is greater than that of a network. If the convergence of a network with a smaller search space matches or approximates that of a complex model, the solution spaces of these two networks may overlap. The essence of knowledge distillation can be described as follows: leveraging label features of the population parameter, a teacher-student model is employed to construct a structurally complex teacher model with strong learning ability. This knowledge is then transferred to a student model with a simpler structure and lower learning ability, enhancing its generalization ability. Furthermore, a compact model involving fewer parameters was selected to achieve accuracy comparable to that of a complex model, thus facilitating model compression. Based on knowledge distillation, knowledge gained from the complex teacher model through training was adopted to guide the student model training on a smaller scale. This enables the student model to emulate the teacher model, effectuating knowledge transfer and model compression.
2. The trained student model is formulated through the mutual superposition of multilayer neural networks. Models with different numbers of network layers exhibit distinct properties and computational complexities. In addition, significant differences were observed in computing resource demands and data sizes across diverse network layers. The student model generated in step (1) was segmented into two sections: one section, with a heavier computational burden, conducted calculations on an edge server, while the other section, involving lighter computations, was processed on the existing terminal device. Additionally, the terminal device collaborated with the edge server system, effectively reducing the computational latency of the corresponding deep model.