# Research on Hybrid Recommendation Model for Personalized Recommendation Scenarios
## 2 Background and Related Work
### RippleNet  
RippleNet [8; 20] is an end-to-end recommendation model framework based on knowledge graphs. Ripple contains two layers: (1) similar to the formation of ripples by water waves in reality, in terms of user portrait construction, the user's historical preferences are used as the center to spread outward layer by layer to form the distribution of the user's potential preferences for items; (2) similar to the gradual weakening of ripple amplitude in reality, the user's preference degree for an entity decreases with the increase of the propagation hops.  
Therefore, RippleNet is analogous to how ripples propagate on the water surface, propagating user preferences on the set of knowledge graph entities and discovering potential user interests along the paths in the knowledge graph to achieve autonomous iterative propagation. It is characterized by inputting user-item pairs, obtaining deep-level possible connections between nodes by mining the correlations in the knowledge graph, and superimposing multiple \"ripples\" obtained from the propagation of users' historical click nodes to form a preference distribution of the user's preference for candidate item nodes, and finally using this preference distribution to calculate the probability of user interaction (e.g., clicking, browsing). In short, the user-item interaction matrix Y and the knowledge graph G are used to obtain the predicted click scores of User u for item v to be selected.  
Knowing the interaction matrix Y and the knowledge graph G, the kth associated entity of User u is defined as  
\[\varepsilon_{u}^{k}=\Big{\{}t\mid(h,r,t)\in G,h\in\varepsilon_{u}^{k-1}\Big{\}} k=1,2,\cdots,H, \tag{6}\]  
where \(\varepsilon_{u}^{0}=\{v\mid y_{uv}=1\}\) indicates that the user has clicked on the item v. Based on (6), the definition of the Ripple set is given:  
\[S_{u}^{k}=\Big{\{}(h,r,t)\mid(h,r,t)\in G,h\in\varepsilon_{u}^{k-1}\Big{\}}k=1,2,\cdots,H. \tag{7}\]  
However, the traditional RippleNet recommendation model has the following shortcomings: (1) The knowledge embedding is simple. In the embedding part of the knowledge graph, a single embedding method is used. The nodes are updated each time, and the outer nodes of potential preferences are prone to low semantic logic, etc. (2) There are problems such as user cold start and information cocoon. The AKTUP [21] model follows the node aggregation mechanism of the RippleNet model to mine user preferences. Still, the method needs to include the path connection between users and target items and complexity and can handle large-scale personalized recommendations. [PERSON] [22] et al. proposed a knowledge water wave graph convolutional network (KRGCN) that uses an end-to-endapproach to obtain higher-order semantic information of the knowledge graph, improves the item feature learning part of the RippleNet model, and uses the convolutional graph network to aggregate higher-order domain information of items. Still, the recommendation results are less interpretable and have the user cold start problem. Moreover, the above improvements for the RippleNet model mainly consider the accuracy aspects of recommendations. Still, with the increase in user demand, users also pay more and more attention to recommendation diversity and put forward higher requirements for mining the relationship between items in personalized recommendations.