## 1 Introduction  
The rise of information technology and the Internet have made it difficult for users to access important information in the face of vast amounts of data. A range of data mining tools have been developed to address the issue of difficult access to important data in the era of information overload. A recommendation system is an essential technique for rapidly filtering and obtaining information from huge amounts of data, particularly in the challenges of personalized recommendation, changing input data, cold data start, and collaborative filtering of related data. Recommendation systems are widely utilized in web applications, ranging from search engines and e-commerce to social networks, media websites, and news portals. In industry, the two-tower model [1; 2; 3; 4; 5; 6] is widely used in recommendation systems in large commercial Internet companies such as Microsoft [1], YouTube [2; 3], Facebook [4], Baidu [5], and Alibaba [6]. Deep learning is an important study topic in the field of machine learning, and it has led to recent advancements in image processing, natural language understanding, speech recognition, online advertising,and recommendation systems, among other areas. Integrating deep learning into recommendation systems and researching how to integrate massive multi-source heterogeneous data to build user models that better match user preferences and needs in order to enhance the performance and user satisfaction of recommendation systems has become the primary objective of deep learning-based recommendation systems. Typical recommender systems include those based on deep neural networks such as RNN [7], CNN [8], GCN [9], and GNN [10]. In recent years, the number of academic literature has increased tremendously, and discovering the required academic literature among many types of literature resource sites has become a crucial aspect of conducting scientific research. In the large amount of literature, it is challenging to locate valuable and actually needed literature that suits the criteria of the field. Currently, information retrieval and recommendation are the most common ways to obtain literature-related data. The information produced by the retrieval method does not reflect the features of the field, does not make use of domain knowledge, does not reflect the personalization of the retrieved information, does not make use of additional correlation information, and the accuracy should be improved. The information obtained by the recommendation system is more accurate, can accommodate more knowledge and information as input features for model computation, and can meet the demand for individualized recommendations. There are numerous approaches for acquiring information in which recommendation systems and information retrieval coexist and support one another. Methods for literature recommendation that incorporate information retrieval, domain knowledge, and other data are crucial for acquiring effective literature. Four groups of literature recommendation approaches can be distinguished: content-based filtering [11; 12], collaborative filtering [13; 14; 15; 16], graph-based recommendation algorithms [17; 18; 19; 4; 10; 11; 12], and hybrid recommendation algorithms [20; 21; 22; 23]. Researchers have paid close attention to the digital resource system. When a search is performed, a large amount of academic literature that meets the search criteria is obtained. Still, not all of them correspond to the corresponding domain, which is indeed required. In earlier studies, content-based filtering, collaborative filtering, and citation relationship-based methods were more prevalent. The content-based filtering approach [24] is simple. The researcher's publications or other information are cited first in the content-based filtering method to construct their profile. Then, the keyword similarity between the researcher's profile and the candidate literature is computed, and the candidate literature is rated accordingly.