# Research on Hybrid Recommendation Model for Personalized Recommendation Scenarios
## 3 NRH Recommendation Model
### Hybrid Forecast
The ROC graph is generally used to judge the merits of binary classifiers, and a graph with FPR (false positive rate) as the horizontal coordinate and TPR (actual positive rate) as the vertical coordinate is obtained, and the larger the AUC value, the better the overall recommendation ability of the algorithm, where the ACC values are calculated as follows:

\[ACC=\frac{TP+TN}{N}, \tag{24}\]

where _TP_ (true positive) indicates that the positive cases are correctly classified as positive cases, _TN_ (true negative) indicates that the negative cases are correctly classified as negative cases, _TP_ + _TN_ indicates the total number of correctly predicted cases in the test sample, and \(N\) represents the number of samples tested. Recommendation diversity is used to measure the variation of items in the user recommendation list. In this paper, the type (genre) of items was used as the basis for classification, and the diversity value was used as the evaluation index for the experiment. Among them, the formula of diversity is shown as follows:

\[diversity=\frac{series\_count(N)}{N}, \tag{25}\]

Information on the dataset used for the recommendation diversity experiment is given in Table 3. \(N\) denotes the first \(N\) items in the user recommendation list, and series_count (_N_) represents the number of item types (genres) in \(N\) items. In the movie, book, and music recommendation datasets, different numbers of objects were selected according to the item types (genres) and targeted to the user groups in the system. Suppose \(N\) = 10 and User u has 10 items in their recommendation list, which covers 6 different item types (genres); then the following calculation is conducted according to the above formula: diversity = 6/10 = 0.6. This means that the recommendation system has a recommendation diversity value of 0.6 for this user, which indicates that the percentage of item types in the recommendation results is 60% with some diversity. Using this item type-based classification basis, the diversity of item types in the recommendation results can be measured. By calculating the diversity value, it is possible to evaluate whether the system can provide diverse item types during the recommendation process and thus judge the performance of the model in terms of recommendation diversity. ### Experimental Results and Analysis

#### 4.3.1 Recommended Accuracy

In this section, we constructed recommendation experiments using NRH recommendation models based on the knowledge graphs provided by movie, book, and music \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **d** & **H** & \(\mathbf{\lambda_{1}}\) & \(\mathbf{\lambda_{2}}\) & \(\mathbf{\lambda_{2}^{\prime}}\) & \(\mathbf{\alpha}\) & **lr** \\ \hline MovieLens-1M & 32 & 2 & \(1\times 10^{-7}\) & \(1\times 10^{-2}\) & \(1\times 10^{-5}\) & 0.4 & 0.02 \\ Book-Crossing & 16 & 3 & \(1\times 10^{-7}\) & \(1\times 10^{-2}\) & \(1\times 10^{-5}\) & 0.6 & 0.025 \\ Last.FM & 32 & 2 & \(1\times 10^{-7}\) & \(1\times 10^{-2}\) & \(1\times 10^{-4}\) & 0.3 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameters of the NRH recommended model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **\#Series** & **\#Items** & **\#Top\_N** \\ \hline MovieLens-1M & 30 & 47,137 & 10 \\ Book-Crossing & 56 & 2568 & 10 \\ Last.FM & 34 & 66 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Basic statistical information about the dataset. recommendation datasets using current mainstream recommendation algorithms as comparisons, including the knowledge-embedding independent learning-based recommendation algorithms CKE and DKN; meta-path-based recommendation algorithms such as PER [17]; classical recommendation algorithms such as LibFM [24] and Wide&Deep [25]; and the knowledge graph-based recommendation algorithms RippleNet model, MKR [26], KGAT [19], KGNN-LS [27], KGCN [28], and CKAN [29]. Knowledge ripple graph convolutional network (KRGCN) was used as a control group.