# Research and Application of Edge Computing and Deep Learning in a Recommender System
## 2 Research Status of the Recommender System  
Collaborative filtering, proposed by [PERSON] et al. [3], signifies that recommender systems, as an independent discipline, attract extensive attention. These systems revolve around the core concept of leveraging the binary relationship between travelers and products to help users discover projects of interest based on their historical behavior and relevant similarities. Content-based recommendations [4], collaborative filtering recommendations [5], and hybrid recommendations [6] represent three major conventional recommendation algorithms [7]. Moreover, recommendation algorithms find applications across diverse websites, including those focused on books, music, videos, news, movies, maps, etc. In recent years, the surge in the popularity of e-commerce applications, including Amazon, eBay, Staples, Dangdang, Douban, and Taobao, is evident, all adopting e-commerce recommendation systems. The incorporation of recommendation systems has not only generated substantial additional revenue for these internet companies but has also elevated user satisfaction and retention. Over the past few years, with the widespread adoption of deep learning, there has been a yearly escalation in articles concerning recommender systems based on deep learning. Numerous universities, research institutes, and enterprises have extensively explored and applied deep learning-based recommender systems. Consequently, this has evolved into a fervently discussed topic in the realm of recommender systems. Given the challenges in obtaining user profiles from a traditional content-based recommender system, [PERSON] et al. [8] extracted user profiles by analyzing their browsing and searching history, thus enriching the user feature representation. In addition, the deep structured semantic model (DSSM) [9] was also extended by them, introducing a multi-view deep neural network. This enhancement allows the multi-view deep neural network to perform item recommendations for users by leveraging semantic matching between two information entities, namely users and items. In essence, this represents an efficient content-based recommendation approach. [PERSON] et al. [10] employed a structure akin to the DSSM, seamlessly integrating comment information into the recommender system to alleviate data sparsity and enhance system quality. Building upon this, they introduced the concept of the deep cooperative neural network (DeepCoNN). [PERSON] et al. [11], relying on deep crossing, proposed a deep and cross network (DCN) model for predicting travelers' advertisement click-through rates. The DCN model utilizes both deep and cross networks for input features, generating pertinent information for prediction. Notably, the DCN combines the advantages of a deep neural network (DNN) and deep crossing. In alignment with deep crossing principles, a deep embedding forest (DEF) model was put forward by [PERSON] et al. [12], which replaced residual units in deep crossing with forest layers. This substitution, along with pre-training, reduced the online prediction time. [PERSON] et al. [13], utilizing multi-source heterogeneous user data, including user profiles, contextual information, historical behavioral data, and item features, proposed a DNN model for recommending YouTube videos. Unlike traditional recommender systems, deep learning-based recommender systems have the potential to automatically learn abstract hidden features of travelers and items through deep learning, incorporating various types of multi-source heterogeneous data. This allows for the modeling of sequence patterns of user behavior, more effectively capturing diverse user preferences and consequently improving the accuracy of recommendations; however, the performance of these deep learning systems relies on more parameters and more profound, broader models. Consequently, such complex deep learning models fail to meet timeliness requirements in various application scenarios.