# Research on Hybrid Recommendation Model for Personalized Recommendation Scenarios
## 3 NRH Recommendation Model
### Hybrid Forecast
In addition, although KGAT overlays multiple attentional embedding propagation layers to convey embedding information, KGAT cannot capture the complex semantics of user-item connections, so its performance is inferior to that of KGCN, RippleNet, KRGCN, and NRH. The NRH recommendation model is more effective than the original model regarding recommendation results, as it improves the item feature learning part of the RippleNet model by using a graph convolutional network to aggregate higher-order items. The NRH uses Node2 vec-side knowledge representation to construct user portraits and fully explores the association relationship between entities through little random wandering, taking into account the homogeneity and isomorphism of nodes in the knowledge graph, reducing the impact of localization of user portrait update in the original model, and making the recommendation results more accurate. CKAN generates recommendations using collaborative filtering techniques. However, standard collaborative filtering techniques may not effectively capture the user's preferences and interests when the user-item interaction data are sparse. As a result, in sparse data, the recommendation effectiveness of CKAN may be constrained. In this study, NRH employed a hybrid recommendation model that could effectively capture users' preferences and interests and produce more individualized recommendations to perform better in the face of scant user activity data. #### 4.3.2 Recommended Diversity

The current research on personalized recommendation algorithms mainly focused on improving recommendation accuracy, the lack of which leads to the frequent appearance of similar types of items and quickly causes the \"information cocoon\" problem. In this section, according to the experimental environment and metrics in Section 4.2, an experimental study on the diversity of recommended items was conducted. The comparison model was the original RippleNet model, and the experimental results under three datasets are shown in Figure 4.

The experimental results demonstrated that the NRH recommendation model provides more diverse recommendations than the conventional RippleNet. Typically, users are distributed across both recommendation models. Nevertheless, the overall standard distribution curve of users under the NRH recommendation model was displaced to the right relative to RippleNet, indicating that when making recommendations to users, the NRH recommendation model tends to be more item-centric. Diversity, precisely the static history portrait, can more accurately characterize users' long-term interests and prefer ences. In contrast, the dynamic preference portrait can more accurately reflect the users' short-term preferences and changing trends, and the combination of the two can more accurately describe the users' interests and requirements. In addition, the separation processing of user portraits enables the NRH model to manage users' historical and current behaviors better, avoiding some unnecessary interference and increasing the diversity of recommendation outputs. According to the box plots in Figure 5, NRH outperformed RippleNet in terms of diversity of recommendation results, as shown by the fact that the median line and box height of NRH boxes were much more significant than RippleNet in the three datasets. The Book-Crossing dataset showed that NRH had a wider variety of box top and bottom edges, indicating a more substantial diversity difference. The recommendation results also spanned a more comprehensive range of genres. The box plot shows that the NRH box length in the MovieLens-1M dataset was comparable to RippleNet. However, the NRH box's upper and lower edges are more significant than RippleNet's, suggesting that NRH's recommendation results contained more high-scoring data points than RippleNet's. This indicates that NRH has an advantage over RippleNet regarding suggestion variety.