# Deep Interest Network Based on Knowledge Graph Embedding
## 4 Experiment
### Performance in CTR  
The result of the hit rate estimation experiment is shown in Table 2. Among them, DINK-K indicates that the user's interaction sequence does not introduce a knowledge graph. At this time, the input vector of the dynamic interesting layer is a random initialized user sequence vector. DINK-A indicates that the model does not introduce attention mechanisms in the dynamic interest in the reinforcement layer. At this time, the dynamic interest in the reinforcement layer is an ordinary GRU model whose input is a polymeric user sequence vector. (-ATT), (-SUM), and (-CAT) indicate the ATTENTION aggregator, SUM aggregator, and CONCAT aggregators, respectively. By analyzing the data analysis of Table 2, the following conclusions can be drawn:  
* DINK is better in the case of data density and high knowledge density  
The scene knowledge map density of MovieLens-1M and Last.fm is 8.04 and 4.03, respectively, the density of the Book-Crossing scene knowledge map is only 1.03, and the user's interaction data are lower. By observing the results, it is possible to find that the performance of DINK in these two datasets exceeds the performance in Book-Cross, whether from the absolute value or relative value. In the Book-Crossing AUC metrics, DINK is backward to the latest GMCF model 6%, while in two datasets in MovieLens-1M and Last.fm, DINK's performance exceeds the rest of the baseline model. Therefore, DINK can use external knowledge to enhance the recommended effect, but compared to the nearest excellent model, there is still no improvement in overcoming data sparseness. Attention aggregator tables in three datasets are due to other aggregators. This is because the attention aggregator fuses the weight information of the relationship edges in the aggregation process, which can effectively extract the features strongly related to the items.  
* Compared to attention mechanisms, neighbor nodes in knowledge maps are more meaningful  
In the three datasets, DINK-K grew more than DINK-A, which means that knowledge representation is a more key promotion of the semantic feature in the study. At the same time, it is noted that DINK-K and DINK-A decline in the Book-Crossing data concentration to a larger degree, indicating that under the sparse scene, the attention mechanism can better play the role, and the external information, such as the knowledge map, is also more important.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**MovieLens-1M**} & \multicolumn{2}{c}{**Last.FM**} & \multicolumn{2}{c}{**Book-Crossing**} \\ \cline{2-7}  & **AUC** & **ACC** & **AUC** & **ACC** & **AUC** & **ACC** \\ \hline Wide\&Deep & 0.898 & 0.820 & 0.756 & 0.688 & 0.712 & 0.624 \\ CKE & 0.801 & 0.742 & 0.744 & 0.673 & 0.671 & 0.673 \\ Ripple & 0.920 & 0.842 & 0.780 & 0.702 & 0.729 & 0.662 \\ LibFM & 0.892 & 0.812 & 0.777 & 0.709 & 0.685 & 0.640 \\ MKR & 0.921 \({}^{*}\) & 0.847 \({}^{*}\) & 0.796 \({}^{*}\) & 0.739 \({}^{*}\) & 0.738 & 0.688 \\ KGCN & 0.917 & 0.837 & 0.796 & 0.728 & 0.731 & 0.678 \\ GMCF & 0.918 & 0.845 & 0.789 & 0.711 & 0.789 \({}^{*}\) & 0.712 \({}^{*}\) \\ \hline DINK-ATT & **0.929** & **0.851** & **0.817** & **0.752** & **0.745** & **0.702** \\ DINK-SUM & 0.925 & 0.851 & 0.813 & 0.745 & 0.730 & 0.688 \\ DINK-CAT & 0.922 & 0.848 & 0.808 & 0.742 & 0.741 & 0.697 \\ DINK-K & 0.903 & 0.826 & 0.782 & 0.742 & 0.718 & 0.668 \\ DINK-A & 0.921 & 0.839 & 0.808 & 0.746 & 0.726 & 0.679 \\ \hline Improve (\%) & 0.5\% & 0.3\% & 2.5\% & 1.7\% & \(-4.2\%\) & \(-1\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance in CTR.