# 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} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MovieLens-1M**} & \multicolumn{2}{c}{**Book-Crossing**} & \multicolumn{2}{c}{**Last.FM**} \\  & **AUC** & **ACC** & **AUC** & **ACC** & **AUC** & **ACC** \\ \hline CKE & 0.796 (\(-14.1\%\)) & 0.739 (\(-13.9\%\)) & 0.674 (\(-10.1\%\)) & 0.635 (\(-10.9\%\)) & 0.744 (\(-10.9\%\)) & 0.673 (\(-12.0\%\)) \\ DKN & 0.655 (\(-29.5\%\)) & 0.589 (\(-31.4\%\)) & 0.621 (\(-17.1\%\)) & 0.580 (\(-18.7\%\)) & 0.602 (\(-27.9\%\)) & 0.581 (\(-24.1\%\)) \\ PER & 0.712 (\(-23.4\%\)) & 0.667 (\(-22.3\%\)) & 0.623 (\(-16.8\%\)) & 0.588 (\(-17.5\%\)) & 0.633 (\(-24.2\%\)) & 0.596 (\(-22.1\%\)) \\ LibFM & 0.892 (\(-4.0\%\)) & 0.812 (\(-5.4\%\)) & 0.685 (\(-8.5\%\)) & 0.639 (\(-10.4\%\)) & 0.777 (\(-6.9\%\)) & 0.709 (\(-7.3\%\)) \\ Wide\&Deep & 0.903 (\(-2.8\%\)) & 0.822 (\(-4.2\%\)) & 0.711 (\(-5.1\%\)) & 0.623 (\(-12.6\%\)) & 0.756 (\(-9.5\%\)) & 0.688 (\(-10.1\%\)) \\ MKR & 0.917 (\(-1.3\%\)) & 0.843 (\(-1.8\%\)) & 0.734 (\(-2.0\%\)) & 0.682 (\(-4.4\%\)) & 0.795 (\(-4.8\%\)) & 0.730 (\(-4.6\%\)) \\ RippleNet & 0.921 (\(-0.9\%\)) & 0.844 (\(-1.6\%\)) & 0.729 (\(-2.7\%\)) & 0.662 (\(-7.2\%\)) & 0.768 (\(-8.0\%\)) & 0.691 (\(-9.7\%\)) \\ KGAT & 0.872 (\(-6.2\%\)) & 0.802 (\(-6.5\%\)) & 0.651 (\(-13.1\%\)) & 0.624 (\(-12.5\%\)) & 0.716 (\(-14.3\%\)) & 0.647 (\(-15.4\%\)) \\ KGNN-LS & 0.913 (\(-1.8\%\)) & 0.840 (\(-2.1\%\)) & 0.689 (\(-8.1\%\)) & 0.635 (\(-10.9\%\)) & 0.795 (\(-4.8\%\)) & 0.726 (\(-5.1\%\)) \\ KGCN & 0.907 (\(-2.4\%\)) & 0.831 (\(-3.2\%\)) & 0.694 (\(-7.3\%\)) & 0.635 (\(-10.9\%\)) & 0.794 (\(-4.9\%\)) & 0.723 (\(-5.5\%\)) \\ CKAN & 0.915 (\(-1.5\%\)) & 0.840 (\(-2.1\%\)) & 0.738 (\(-1.5\%\)) & 0.655 (\(-8.1\%\)) & 0.831 (\(-0.8\%\)) & 0.744 (\(-2.9\%\)) \\ KRGCN & 0.903 (\(-2.8\%\)) & 0.841 (\(-2.0\%\)) & 0.730 (\(-2.5\%\)) & 0.666 (\(-6.6\%\)) & 0.805 (\(-3.6\%\)) & 0.738 (\(-3.5\%\)) \\ NRH & **0.929** & **0.858** & **0.743** & **0.713** & **0.837** & **0.765** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of AUC and ACC of different models under each dataset. three datasets. The main reason is that the model adopts the Node2 vec-side algorithm for item portrait modeling, profoundly explores the association relationship between items, divides the user portrait into two parts (static history portrait and dynamic preference portrait), and uses hybrid recommendations to enhance the accuracy of recommendations. By comparing the experimental results, it was found that compared to the traditional recommendation algorithms CKE and DKN that adopt the translation model (Trans series) as the representative knowledge-based embedding, MovieLens-1M and NRH improved the AUC and ACC metrics by 13 percentage points and 12 percentage points, respectively, compared with CKE, and they improved the AUC and ACC metrics by 27 percentage points, respectively, compared with DKN. This may be because CKE adopts a translation-based model as its knowledge representation, which uses independent learning to represent knowledge graph entities and relationships, thus ignoring the network structure characteristics of the knowledge graph itself and making it challenging to discover the correlation relationships among entities. At the same time, DKN was initially applied in the field of long-text news recommendation and tends to analyze text content only when the text content contained in the entities is long enough to ensure its recommendation accuracy. In the experimental dataset involving entities, relationships, and attributes with short ranges, DKN cannot obtain valuable information, which also causes its results to be biased. Compared with the MKR model, the model in this paper effectively exploits the neighborhood relationships among entities in the knowledge graph and can extract user features effectively compared with the KGCN model.