# Research and Application of Edge Computing and Deep Learning in a Recommender System
###### Abstract  
Recommendation systems play a pivotal role in improving product competitiveness. Traditional recommendation models predominantly use centralized feature processing to operate, leading to issues such as excessive resource consumption and low real-time recommendation concurrency. This paper introduces a recommendation model founded on deep learning, incorporating edge computing and knowledge distillation to address these challenges. Recognizing the intricate relationship between the accuracy of deep learning algorithms and their complexity, our model employs knowledge distillation to compress deep learning. Teacher-student models were initially chosen and constructed in the cloud, focusing on developing structurally complex teacher models that incorporate passenger and production characteristics. The knowledge acquired from these models was then transferred to a student model, characterized by weaker learning capabilities and a simpler structure, facilitating the compression and acceleration of an intelligent ranking model. Following this, the student model underwent segmentation, and certain computational tasks were shifted to end devices, aligning with edge computing principles. This collaborative approach between the cloud and end devices enabled the realization of an intelligent ranking for product listings. Finally, a random selection of the passengers' travel records from the last five years was taken to test the accuracy and performance of the proposed model, as well as to validate the intelligent ranking of the remaining tickets. The results indicate that, on the one hand, an intelligent recommendation system based on knowledge distillation and edge computing successfully achieved the concurrency and timeliness of the existing remaining ticket queries. Simultaneously, it guaranteed a certain level of accuracy, and reduced computing resource and traffic load on the cloud, showcasing its potential applicability in highly concurrent recommendation service scenarios. recommender system; knowledge distillation; deep learning; edge computing; teacher-student model +
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deep learning is widely employed in recommendation systems by research institutions and companies, exemplified by Google's \"Wide&Deep\" recommendation system [2]. This system has designed a model that integrates several modules, including DeepFM, DCN, XDeepFM, and AutoInt. While these models enhance intelligent recommendations to a certain extent, the heightened accuracy of deep learning depends on its broader and deeper network structure. As this complex network structure is constructed to extract feature vectors from extensive and redundant samples, these models possess numerous parameters individually; however, their hardware resources limit the training speed for computing, and they fall short of meeting real-time high-concurrency predictions. In practice, the time consumed by such models progressively lengthens, posing a challenge for their absolute online deployment. As the world's largest internet railway ticketing system, 12306 boasts 680 million registered users and a daily ticketing capacity exceeding 20 million tickets. The system's daily traffic can reach 53.2 billion user times, equivalent to over 616,000 people browsing tickets per second. Within 12306's ticketing system, the response time for spare ticket queries significantly influences passengers' experience and the effectiveness of the online response system. In this context, a complex deep learning model fails to meet the demands of highly concurrent query requests or operates on a mobile terminal or an embedded device. Consequently, the model requires compression and acceleration to address these challenges. Moreover, a smart ranking model of the spare tickets list on the traveling platform has been developed based on knowledge distillation. This approach aims to reduce the model's complexity, enabling the training of a unified model in different teacher models or a set of them, resulting in more accurate rankings.