# Research on Hybrid Recommendation Model for Personalized Recommendation Scenarios
## 3 NRH Recommendation Model
### Hybrid Forecast
Additionally, according to the experiments, \(\alpha\) had different effects on the recommendation performance of other datasets. FM was more influenced by \(\alpha\). Figure 6: AUC and ACC for different Hop: (**a**) experimental results of MovieLens-1M; (**b**) experimental results of Book-Crossing; (**c**) experimental results of Last.FM. Figure 7: AUC and ACC of different \(\alpha\): (**a**) AUC for different \(\alpha\) under each dataset; (**b**) ACC for different \(\alpha\) under each dataset. ### Time Complexity Analysis

The time cost of NRH is mainly composed of two parts, namely, preprocessing and model training, where the preprocessing part primarily uses Node2 vec-side for the construction of item portraits, and node2 vec-side fused knowledge representation adds relational representation and diversified wandering strategies based on Node2 vec. The average time complexity of node2 vec random sampling for each sampling point is \(O\left(\frac{l}{k(l-k)}\right)\), the time complexity of the skip-gram part using hierarchical softmax optimization is \(O(\log_{2}N_{v})\), and the time complexity of the relational aggregation part is \(O(rlN_{v})\). The time complexity of the Node2 vec-side preprocessing stage is \(O\left(\frac{2 lN_{v}}{k(l-k)}+2\log_{2}N_{v}+2 rlN_{v}\right)\) under the case of using two wandering strategies: \(l\) denotes the length of random wandering, \(k\) means the number of domain nodes, \(N_{v}\) represents the number of nodes, and \(r\) represents the random wandering number of times. The model training part was mainly combined with RippleNet for update iterations, so the time complexity was similar to RippleNet, and the average running time comparison between NRH and RippleNet is given in Table 6.

Compared to the RippleNet model, NRH has some drawbacks regarding temporal complexity. The NRH model introduces a Node2 vec-side item painting representation stage, which increases the preprocessing part of the model time cost but in the model training stage, which is the main reason why the preprocessing time of the NRH model is substantially longer than that of the RippleNet model. However, adding Node2 vec-side item pictures does not increase the processing time NRH. However, it can lower the number of training epochs, which can be considered in the future to enhance the NRH model's preprocessing procedure and decrease the preprocessing time overhead. ## 5 Conclusions

In user portrait localization, the conventional RippleNet recommendation model changes the user preference set by randomly selecting favored items. In contrast, the suggested NRH recommendation model deeply examines the association relationship between items and leverages Node2 vec-side for item portrait modeling. In contrast, the static history portrait and dynamic preference portrait based on user history information are separated during the user portrait construction stage, which is beneficial in removing the issue with the localization of user portrait updates. A hybrid recommendation method based on collaborative item filtering and knowledge graph can solve the localization problem of traditional user profile updates and improve the accuracy and variety of personalized recommendations. The user profile construction stage is divided into two parts based on user history information, static history profiles, and dynamic preference profiles. The deep association relationships between nodes can be better explored using the NRH model proposed in this research. This model is primarily applicable to personalized recommendation situations that need to balance diversity and accuracy, such as fields for music and movie recommendations.