# Research on Hybrid Recommendation Model for Personalized Recommendation Scenarios
## 2 Background and Related Work
### Knowledge Graph-Based Recommendation Algorithm  
The current research direction of knowledge graph-based recommendation algorithms is mainly to enhance the accuracy of recommendations by knowledge embedding [10; 11; 12; 13; 14; 15; 16], path [17; 18] or higher-order information aggregation [19; 20; 21; 22; 23; 24], etc. [PERSON] [10] et al. used the Metapath2 Vec [11] model in the knowledge graph to introduce entity nodes to improve the recommendation result accuracy; in 2016, [PERSON] et al. proposed the CKE [13] model by fusing collaborative filtering and the knowledge graph feature learning (TransR [12] model). [PERSON] [14] used TransE [15] as a knowledge embedding model to describe user-item preferences; in 2018, [PERSON] [16] et al., based on KCNN-embedded text word, proposed the deep knowledge-aware network (DKN). Similar recommendation algorithms based on knowledge embedding require preprocessing operations on the knowledge graph, using knowledge embedding to input relevant characteristics of items into the neural network. The recommendation model eventually learns and understands the knowledge structure inherent in the knowledge graph. Although the knowledge embedding-based approach is flexible and facilitates the use of models for migration in multiple scenarios, the lack of utilization of higher-order information leads to a limited representation of entities.  
The path-based knowledge graph recommendation algorithm started earlier. [PERSON] et al. proposed a heterogeneous information network method in 2014 [17], which uses matrix decomposition to obtain implicit features of users and items of different meta-paths for recommendation; based on this, [PERSON] et al. proposed a meta-graph-based recommendation method in 2017 [18], and the main idea is to use meta-graphs to represent the higher-order recommendation semantics and fully exploit its structural features by constructingmeta-graph to make up for the shortage of the meta-path. Both approaches represent the relationship between users and items by extracting meta-paths or meta-graphs. The path-based recommendation approach aims to establish the path connection between user-items, which enhances the interpretability of recommendation results. Still, the quality of the mined path instances seriously affects the final recommendation results.  
Knowledge graph recommendation algorithms based on higher-order information aggregation (e.g., KGAT [19]) aim to combine semantic information with paths to enrich the representation of users and items by surrounding neighborhood nodes. Similar recommendation algorithms only characterize the knowledge graph. Still, the recommendation results need to be more comprehensive regarding interpretability and logic. Hence, [PERSON] et al. first proposed the concept of \"preference diffusion\" and thus offered the RippleNet [8; 20] model. The core idea of the RippleNet model is to propagate user preferences on the knowledge graph and then continuously portray user profiles through the semantic relationships between entity nodes and nodes in the propagation process. It has good recommendation performance and is widely used.