# Multi-Feature-Enhanced Academic Paper Recommendation Model with Knowledge Graph
## 3 Approach of This Paper
### Knowledge Graph Information  
#### 3.3.1 Knowledge Graph Construction  
This section of the paper focuses on constructing a knowledge graph from the dataset to capture the relational information between papers and users, as well as to enhance semantic expression. This paper treats papers and users as entity nodes, constructing separate knowledge graphs for papers and users based on various relationships between these entities as edges. For the paper knowledge graph, the focus is on analyzing citation relationships, research topics, and publication information. For the user knowledge graph, the emphasis is on capturing the evolution of user interests, publication history, and citation behavior. The knowledge graph enriches the recommendation system with contextual and associative information, allowing for a deeper understanding of paper content, a precise identification of user interests, and the discovery of direct and indirect connections between entities. The knowledge graph is defined as \(G=\{(h,r,t)\mid h,t\in E,r\in R\}\), where each triplet denotes a relationship \(r\) between the head entity \(h\) and the tail entity \(t\). E represents the set of entities, and R represents the set of relationships. Initially, the TransR [31] model is utilized to process the complex information within the knowledge graph. TransR addresses complex relationships and entity polysemy in knowledge graphs by assigning separate vector spaces to entities and relationships and using transformation matrices, which improves the accuracy of the knowledge graph. To construct the paper and user knowledge graphs, this paper first obtains initial vectors from the knowledge graph. For users, as illustrated in the UERM module in Figure 1,an initial user vector is derived by attention-weighting the papers interacted with by the user and calculating the average of these weighted paper vectors. This vector provides a comprehensive reflection of the user's interests. Following the method in Section 2.1, user temporal information is integrated by analyzing the user interaction sequence to capture the evolution of their interests and behavioral patterns. This temporal information offers a dynamic perspective, enabling the recommendation system to better understand the user's changing needs over time. Additionally, other user attributes, such as research fields and institutions, are considered and converted into embedding vectors through an embedding layer, further enriching the semantic content of the user vector. Finally, by combining the initial vector, the temporal information representation \(S_{u}\), and other attribute embedding vectors with a multi-layer perceptron (MLP) module, this paper achieves a more comprehensive and refined initial node vector for the user knowledge graph. For papers, the initial node vector in the paper knowledge graph is obtained by multiplying each sentence vector by its corresponding importance weight, derived from the ALBERT and LSTM processing of titles and abstracts, and then summing these weighted sentence vectors. The initial relationship vectors are derived, as described in Section 3.3.1, by extracting the feature information of nodes and edges from the knowledge graph, along with their attributes. Ultimately, using the formula presented in Section 3.3.2, referred to as Equation (20), the edge vector \(f_{e}\) is obtained, which represents the initial relationship vector \(r\) within the knowledge graph. Given the initial entity vector \(e\) and the initial relationship vector \(r\), the entity vectors projected via the TransR method are denoted as \(e^{\prime}\), and the relationship vector as \(r^{\prime}\), with the projection matrix \(M_{r}\). TransR learns the embedding vectors for entities and relationships by optimizing a principle based on the translational algorithm's fundamental premise (\(e^{\prime}_{h}+r\approx e^{\prime}_{t}\)), which establishes relationships through projection. The projected paper entity vector representation is expressed as \(e^{\prime}=e\cdot M_{r}\).