# Research on Hybrid Recommendation Model for Personalized Recommendation Scenarios
## 3 NRH Recommendation Model
### Hybrid Forecast
In comparison, RippleNet's lower upper and lower box margins may suggest that some of the recommendation results contained data points with lower scores or some outliers, leading to a significantly lower level of suggestion quality. NRH includes a disproportionate number of outlier points in the Last.FM dataset, which suggests that the recommendation results have more rare or uncommon types. The Last.FM dataset, which deals with music recommendations, includes people with vastly different musical tastes, which could be a contributing factor. Due to this variation, it may be challenging for the NRH model to faithfully represent all users' tastes, resulting in some uncommon or odd suggestions. To

Figure 4: Experimental results of diversity under each dataset: (**a**) experimental results of MovieLens-1M; (**b**) experimental results of Book-Crossing; (**c**) experimental results of Last.FM. improve the diversity of recommendation results, NRH uses Node2 vec-side for item portrait representation and network structure information to learn node embeddings, keeping the distance between neighboring nodes in the embedding space as close as possible. ### Ablation Experiments

Table 5 illustrates the impact of various components on the model's recommendation performance. The Node2 vec-side knowledge representation, static history portrait of users, and hybrid push mode affected the model's recommendation performance. In the case of RippleNet-based recommendations, the introduction of Node2 vec-side knowledge representation improved the average accuracy and AUC by 0.89 and 0.72 percentage points, respectively, on the three datasets. This is likely because the model could better capture the relationship and contextual information between nodes after introducing Node2 vec-side knowledge representation. The average precision and AUC of the model improved by 2.94 and 1.24 percentage points, respectively, after introducing both Node2 vec-side knowledge representation and a static history portrait of users. This is likely because the introduction of a static history portrait of users could fully account for the historical behaviors and preferences of users. After incorporating Node2 vec-side knowledge representation and the hybrid recommendation model, the average accuracy and AUC of the model increased by 4.64 and 2.24 percentage points, respectively. The hybrid recommendation model may provide more complete and accurate results. The diversity and scope of recommendations can be expanded by combining multiple methods. ### Hyperparametric Analysis

In this section, we focus on the effects of the parameters Hop and \(\alpha\) on the model in NRH. As can be seen in Figure 6, the higher the Hop value, the better. For the MovieLens-1M dataset and the Last.FM dataset, the model performed best when Hop was at 2 (i.e., when the number of jumps was at two hops), while the Book-Crossing dataset was at a Hop value of 3. The best results are recommended. It can be seen from Figure 7 that the model

Figure 5: Comparison of RippleNet and NRH recommended diversity. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Modules** & & & **MovieLens-1M** & & **Book-Crossing** & **Last.FM** \\
**Node2 vec-Side** & **Static** & **Hybrid** & & & **AUC** & **ACC** & **AUC** & **ACC** \\  & **Portrait** & **Forecast** & & & & & \\ \hline \(\surd\) & & & 0.923 & 0.845 & 0.734 & 0.670 & 0.778 & 0.701 \\ \(\surd\) & \(\surd\) & & 0.925 & 0.850 & 0.737 & 0.686 & 0.786 & 0.722 \\ \(\surd\) & & \(\surd\) & 0.927 & 0.851 & 0.735 & 0.696 & 0.809 & 0.746 \\ \(\surd\) & \(\surd\) & \(\surd\) & 0.930 & 0.856 & 0.742 & 0.711 & 0.835 & 0.753 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Effect of different components on model recommendation results. performed best under different datasets with different \(\alpha\), indicating that the weighting of the two recommendation methods had additional adaptability and did not work best under the average distribution.