## 1 Introduction
As a result of this issue, researchers can obtain literature that is irrelevant to their research domain. This research proposes a heterogeneous network literature recommendation method based on domain knowledge graphs and hotspot information composition to overcome the above-mentioned issues. In this paper, we argue that the knowledge network of domain knowledge involved in search information helps recommend more relevant literature and that the network of hotspot information involved in search information helps recommend literature related to current query domain hotspots. Based on the authors' earlier work [31], the composition of the domain knowledge graph [31] has been accomplished. This paper extracts the relevant portion of the knowledge graph in the existing domain knowledge graph based on the query words and constructs the query-related domain knowledge triad. Similarly to the preliminary work I completed on the hotspot information network, this paper obtains the relevant portion of the hotspot sub-network according to the query term in the existing hotspot network, which constitutes the hotspot triad related to the query term, employs the triad fusion method to fuse the relevant two types of triads [32] into the corresponding query vector, and calculates the similarity between the query vector and the document vector in the candidate set, as shown in Figure 1.  
The research objective of this paper is to provide a more accurate method for searching domain literature that reflects the characteristics and hot information of the domain given the query words or phrases. The main contributions of this paper are as follows. * To provide a more accurate method for searching domain literature that reflects the characteristics and hot information of the domain. A heterogeneous network literature recommendation method is proposed based on the domain knowledge graph and  
Figure 1: A novel scientific paper recommendation model via heterogeneous network embedding-based knowledge graph and hotspots. hotspot information composition. The combined effect of the domain knowledge graph and hotspot information is considered in the heterogeneous literature network for the first time. It overcomes the problem that the recommended literature needs more domain performance and reflects the hotspots and frontiers of literature. The effect of domain knowledge and hotspot information in literature recommendation is demonstrated. Experiments further validate the effectiveness of the method. * The use of a proven two-tower retrieval system is appropriate. How to construct models that reflect domain characteristics and hotspot information under the two-tower system, thus enriching the background information of query terms, is a problem worthy of study. This paper proposed a triplet fusion method on a two-tower retrieval model. It is challenging to map the fusion of heterogeneous networks consisting of knowledge graphs and hotspot networks into the same hidden space. In this paper, based on the popular two-tower retrieval model in industry, which can ensure the basic performance of the model at a reasonable level, the knowledge graphs and hotspots related to query terms are fused into a unified query vector as a query set, which is ranked and recommended after similarity calculation with the candidate set. * The search process was full of candidate documents. How to filter the enormous amount of candidate documents initially, select the appropriate set of candidates, and reduce the model's computation is a problem worth studying. This paper proposed a prospective literature extraction approach based on hotspots and knowledge graph information. By calculating the document similarity, the documents associated with the query word hotspot information or knowledge graph are extracted, reducing the number of irrelevant documents in the candidate document collection and increasing the matching efficiency. * The experiment demonstrated the validity of our proposed model PRHN.