# Research and Application of Edge Computing and Deep Learning in a Recommender System
## 5 Conclusions  
Given the rapid advancements in information technologies and Internet products, the issue of information overload is escalating. In the realm of rail transit, passengers' incessant demands for relevant and valid information are on the rise. Recommender systems are assuming an increasingly pivotal role during the digital transformation in various fields. In comparison to traditional recommendation algorithms, deep learning-based recommender systems have the capability to (1) fuse various types of multi-source heterogeneous data; (2) extract high-level features of big data; (3) automatically perform embedding for features of passengers and products; (4) acquire a valid representation of data space or features; and (5) effectively reflect the potential preferences of passengers and characteristics of products. In this way, the performance of the relevant recommendation models can be improved; however, as the model performance advances the structure of the deep learning network becomes more complex. Model training and computing require significant network, storage, CPU, and memory overhead, posing significant constraints on deep learning applications in resource-limited environments or high-concurrency real-time recommendation scenarios. To boost the precision of the recommender system, reduce the complexity of the deep learning model, and improve the concurrency as well as timeliness of relevant computing, classic deep learning-based recommendation models, such as DeepFM, the DCN, and xDeepFM, were used as teacher models. These were employed to construct a multi-teacher model based on the principles of knowledge distillation, subsequently used to train the user-defined student model. This approach, undertaken to ensure recommendation accuracy, facilitated the structural compression of deep learning models. Additionally, the student model was segmented based on edge computing principles, allowing certain computations to be migrated to a mobile terminal for collaborative computing between a data center and a terminal device. This effort generated a recommendation list, improving data transmission efficiency, reducing traffic load, optimizing cloud resource allocation, and enhancing the timeliness as well as concurrency of the recommendation model. Finally, the proposed model underwent testing for train number list recommendation on the 12306 ticketing system. A comprehensive analysis found that the proposed model performs excellently in terms of precision, recall rates, F1 scores, timeliness, and high concurrency. In practical applications, this research methodology proves feasible and holds significant value in model deployment as well as real-time production line applications. In authentic learning scenarios, students not only learn from correct answers and teacher guidance but also through interactions with their peers, a crucial aspect in increasing their  
Figure 6: Concurrent access comparison. learning ability. In future research, it is advisable to explore the integration of multiple student models within the knowledge distillation framework to enhance the efficiency of these models. Simultaneously, the authors intend to conduct further investigations into the application of deep learning models within an edge computing architecture, aiming to enhance the prediction accuracy and efficiency of residual ticket queries in the 12306 ticketing system. It is worth noting that the 12306 ticketing system encompasses not only train ticket sales but also various travel services such as hotels, tourism, and catering; however, practical challenges may arise due to the potential isolation of information by different data holders, leading to a data isolation problem. Subsequent research efforts should be directed towards addressing and resolving this issue [30].