# The Use of Attentive Knowledge Graph Perceptual Propagation for Improving Recommendations
## 5 Experiments
### Experimental Results
#### 5.4.2 Performance Comparisons with Variants  
To further validate the effectiveness of the components of the AKGP framework, we compared three AKGP variants using ablation experiments. AKGP/E ablates parts of the knowledge graph embedding methods, fine-grained modeling and the extraction of contextual information. The entities and relationships in the user, item and knowledge graphs are represented by only one fixed-length embedding vector. AKGP/A represents the removal of the attention network from the AKGP framework. AKGP/S does not use hierarchical sampling methods, and instead uses direct summation pooling whereby it simply sums the pooling of domain information. Table 4 shows the results of the AUC metrics for the three variants of the model. From the experimental results, it is clear that AKGP/E has a significant performance loss in all three datasets, which indicates that the user-item representations obtained from coarse-grained modeling are suboptimal and do not result in the best recommendation performance. The AUC metrics of AKGP/A and AKGP/S show a decrease in all three datasets, which reflects the importance of distinguishing domain information when aggregating domain information in attention networks, and the importance of hierarchical sampling optimization. In summary, every part of the AKGP framework is necessary, and the results obtained after removing any of those parts are suboptimal. Recommendation systems generally face the challenges of cold-start and data sparsity problems. The cold-start problem refers to the lack of sufficient interaction data to capture the interest of new users, which affects the accuracy of recommendations. The data sparsity problem refers to the fact that many users have only interacted with a few items, which leads to the inability of recommendation systems to provide accurate recommendations to users. To investigate whether the AKGP model can mitigate the effects of the cold-start problem and the data sparsity problem, the training data were divided into five equal sub-data of 20%, 40%, 60% and 80%, and the comparison results are shown in Figure 8. The results prove that the AKGP model performs well, especially in the case of sparse interaction records, and the improvement decreases as the training data increases. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Last-FM** & **Book-Crossing** & **MovieLens-20M** \\ \hline AKGP/E & 0.788 & 0.702 & 0.898 \\ AKGP/A & 0.723 & 0.642 & 0.846 \\ AKGP/S & 0.791 & 0.727 & 0.912 \\ AKGP & 0.829 & 0.742 & 0.976 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of performance among variations of AKGP. Figure 7: The results of Recall in Top-K recommendation. (**a**) Results using the Last-FM dataset. (**b**) Results using the Book-Crossing dataset. (**c**) Results using the MovieLens-20M dataset. In AKGP, knowledge graph embedding methods, such as TransE [49], TransH [50], TransR [40] and TransD [44], are used for knowledge graph complementation tasks to extend the potential relationships between knowledge graph entities. The effects of using different representation learning methods on the AUC are shown in Table 5. The experiments show that using TransD for the representation learning of knowledge graph entities in AKGP is more effective than using other methods. TransD is the most complex model among the four embedding methods. It maps entity embeddings to the corresponding relationship space and learns low-dimensional representation vectors for each entity and relationship to preserve the structural information of the knowledge graph. The effect on the AUC of changes in propagation depth in the multilayer propagation layer of AKGP is shown in Table 6 and Figure 9. From the experimental results, the performance of the AUC metric is optimal when the propagation depths of the three data sets are 1, 3 and 2.