# Research on Hybrid Recommendation Model for Personalized Recommendation Scenarios
## 3 NRH Recommendation Model
### Hybrid Forecast
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Preprocessing (s)**} & \multicolumn{4}{c}{**Training Time(s)**} \\ \cline{4-7}  & & & **Average Per** & **Epoch** & **Training Cost** & **Total Time (s)** \\ \hline \multirow{3}{*}{MovieLens-1M} & RippleNet & 5.81 & 11.27 & 60 & 676.2 & 682.01 \\  & NRH & 496.83 & 11.85 & 20 & 237 & 733.83 \\ \multirow{3}{*}{Book-Crossing} & RippleNet & 2.48 & 2.06 & 50 & 103 & 105.48 \\  & NRH & 796.41 & 2.21 & 20 & 44.2 & 840.61 \\ \multirow{3}{*}{Last.FM} & RippleNet & 0.21 & 0.54 & 30 & 16.2 & 16.41 \\  & NRH & 14.40 & 0.54 & 10 & 5.4 & 19.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of time consumption of NRH and RippleNet models. Nevertheless, this study also identifies some NRH flaws. Due to the complex structure of the model and the increase in the number of parameters, NRH models have a higher computational cost and complexity than traditional models. In addition, NRH models have unique requirements for feature engineering and pre-processing steps, which may significantly increase computational resources and time overhead when applying NRH models to large datasets. The NRH model's performance could suffer if the suggestion data are erratic or not closely aligned with user preferences. To ensure the accuracy and reliability of the recommendation data, thorough data cleaning and feature selection must be made before NRH models are used. Additionally, NRH models are challenging to interpret and must inform users how to evaluate recommendations. Further, many domain and dataset features may impact NRH performance. We can think about further mining the knowledge graph node information, mining the association relationship between nodes by adding more node features, optimizing and accelerating the training and recommendation process of the model to reduce the computational cost and time overhead, allowing users to understand the reason for and basis of recommendation results using visualization and user interface design, etc., as well as performing cross-domain. Additionally, we take into account enhancing user involvement and feedback methods as well as modifying models through active learning to match users' unique demands. Last, we concentrate on algorithm fairness, create suitable assessment measures and fairness constraints, and minimize bias against particular users or items. [PERSON] designed the proposed method and wrote the paper; [PERSON] and [PERSON] wrote the code and performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] modified the paper and offered support. All authors have read and agreed to the published version of the manuscript. This research was funded by the Double-class Special Project on Cyberspace Security and Law Enforcement Technology of the Chinese People's Public Security University (2023 SYL07). Not applicable. The authors thank the Major Project of Basic Research Business Fund of People's Public Security University of China in 2021 (2021 JKF105) and the Project of Building a High-level Non-Staff Institution of People's Public Security University of China (2021 FZB13) for covering the costs to publish open access and the costs incurred when writing this study. In addition, the authors thank the anonymous reviewers for their insightful comments that helped improve the quality of this study. The authors declare no conflict of interest. ## References

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