# Multi-Feature-Enhanced Academic Paper Recommendation Model with Knowledge Graph
## 1 Introduction  
With the rapid development of information technology and the internet, human society has quickly entered the information age. The application of technologies such as cloud computing, the Internet of Things, and big data on the internet has not only greatly enriched data resources but also led to a geometric increase in data scale [1; 2]. This phenomenon has promoted the emergence of new disciplines and interdisciplinary integration, thereby driving an explosive growth in the number of academic papers [3]. Researchers in the academic community are therefore facing an unprecedented challenge of information overload [4; 5] as they need to search and filter relevant information from a vast array of literature, which undoubtedly increases the difficulty and time cost of acquiring knowledge [6]. To effectively address this challenge, academic paper recommendation systems have emerged [7] that aim to help researchers by alleviating the burden of information overload through advanced computing and ranking mechanisms and recommending papers closely related to their research interests and focuses [8]. Although traditional recommendation methods, such as collaborative filtering and content-based filtering, have achieved some success [9], they still have shortcomings in terms of recommendation efficiency and accuracy when dealing with large-scale datasets. These methods often fail to fully utilize key content such as the text information of papers (e.g., titles, abstracts, etc.) and the citation relationships between papers [10]. In addition, they also face issues like data sparsity and cold starts, which limit their ability to provide personalized recommendations.  
In recent years, recommendation models based on deep learning and graphs have received widespread attention for their potential in integrating multi-source information and have made significant progress in the field of academic paper recommendation [11; 12]. In particular, the introduction of knowledge graphs has brought a new perspective to recommendation systems. As a structured semantic knowledge base, knowledge graphscan richly represent the relationships between entities and provide a new method of data representation and reasoning for recommendation systems.  
The present work aims to explore how to enhance the accuracy and personalization of academic paper recommendation systems by integrating deep learning, knowledge graphs, and strategies for the fusion of temporal and relational information. A feature-enriched paper recommendation model that integrates knowledge graphs is proposed that not only utilizes attention mechanisms to deeply mine the text and relational features of papers but also integrates the temporal information of paper publication and citation, as well as the edge information in the academic citation network. Through this multidimensional feature fusion, the model presented in this paper can more accurately capture the dynamic research interests of users and the complex academic relationships between papers. The main contributions of this paper are as follows:  
1. The model presented in this paper integrates textual and relational features of academic papers, fusing temporal information of publication and citation, along with edge information from the academic citation network, to construct a more representative vector representation of papers.  
2. This paper's model introduces advanced neural network architectures and attention mechanisms to effectively capture the temporal information of users' behavioral patterns and the complex relationships within the academic network, thereby generating a more accurate representation of user interest features.  
3. The effectiveness of the proposed model has been validated through experiments. Compared to traditional recommendation algorithms and baseline models, the model has demonstrated improved accuracy and personalization in recommendations.