## 3 Methodology
### Problem Formulation  
Input: For query terms _Query_ abbreviated as \(Q\). Output: Let the set of the domain papers be \(P\). Find the documents from the collection of articles whose first \(n\) items are closest to \(Q\). Because the query term _Query_ contains limited information and does not fully reflect domain characteristics and domain hotspot information, we extend the domain knowledge and extract the corresponding hotspot information in this paper to form a domain knowledge graph and a domain hotspot graph for the query term _Query_. The mapping information is used to construct the query vector for calculating similarity with the literature set. We clustered the entities in the knowledge graph that the authors' previous research created. The entities in the categories similar to the query term _Query_ and the relationships between the entities served as the query term's extended domain knowledge. We clustered the hotspot entities formed by the authors' previous research. The entities similar to the query term _Query_ and their relationships were used as the query term's extended hotspot context. By query terms _Query_, extended domain knowledge, and extended hotspot background, our model \(f\) recommends the top \(n\) items in the candidate set of documents \(p_{n}\). \[\tilde{p}_{n}=f(\textit{Query},\textit{KnowledgeGraph},\textit{HotspotGraph}, \textit{PaperSet},\theta) \tag{1}\]  
where \(\tilde{p}_{n}\) is the top \(n\) items predicted in the literature recommendation algorithm, \(f\) is the literature recommendation model method, _Query_ is the query term, \(\textit{KnowledgeGraph}\) is the domain knowledge graph constructed based on query term _Query_ expansion,  
Figure 2: Example of literature recommendation model  
\(H_{\textit{H}spotGraph}\) is the hotspot graph constructed based on query term \(\textit{Query}\) expansion, and \(\textit{PaperSet}\) is the set of query candidates for query term \(\textit{Query}\). The objectives of the model are:  
\[\arg\min_{\theta}\ \sum_{q\in\textit{Query}}\sum_{j\in\textit{Paper}}p_{q,j}- \bar{p}_{q,j} \tag{2}\]  
where \(p_{q,j}\) is the labeling score of query term \(\textit{Query}\) for candidate \(j\) and \(\bar{p}_{q,j}\) is the prediction score of query term \(\textit{Query}\) for candidate \(j\). Construction of Knowledge Graphs and Domain Hotspot Information Networks Related to Query Terms Query  
The effective extension of query terms \(\textit{Query}\) to introduce domain knowledge and hotspot information related to query terms \(\textit{Query}\) as input information into the model is the highlight of this paper. It is a challenging problem to establish rich and effective domain knowledge and hotspot information. The domain knowledge graph, which corresponds to domain knowledge related to query terms \(\textit{Query}\), and the hotspot relationship graph, composed of hotspots mined from the literature by the author's previous research, are fused into the graph model in this paper. The model can reflect the domain characteristics and hotspots by incorporating the knowledge graph and the hotspots information network. \[KnowledgeGraph=\{(h,r,t)|h,t\in\textit{Entits},r\in R\} \tag{3}\]  
where \(\textit{Entits}\) represent the set of entities associated with the query vector, and relations represent the set of relations. Cosine similarity is used to compute similarity in this case. We compute similarity for the entities in the domain knowledge graph constructed by the author's previous work [31]. Using \(\textit{CosineSimilarity}=\textit{CosineSimilar}(query,entity)\) and \(\textit{CosineSimilarity}>0.5\), we consider the domain entity to be related to the query term; the entity and the entity association relationship formed on the graph and the entity at the other end are saved, i.e., the corresponding \((h,r,t)\) is saved.