# Knowledge-Aware Enhanced Network Combining Neighborhood Information for Recommendations
## 4 Experiments
### Experimental Results  
In this section, we conduct experiments on click-through rate prediction for different models. We show the final results in Table 3. The analysis is as follows:  
* Compared with other baseline models, the performance of PER is relatively poor, because the design of artificial paths often requires more professional domain knowledge, resulting in its inability to use the information in the KG efficiently.
* From the experimental results, LibFM, Wide&Deep, and CKE, achieved better experimental results than PER, indicating that they can utilize the rich auxiliary information in the KG to improve the recommendation performance.
* RippleNet shows excellent performance, suggesting that obtaining auxiliary information by propagating user preferences in the KG is effective in improving the recommendation effect. However, by comparing the experimental results of the three datasets, RippleNet performs poorly on the dataset with greater data sparsity, indicating its strong dependence on data sparsity.
* For both MKR, CAKR, and KGCN, MKR and CAKR outperform all baseline methods on the Book-Crossing dataset, suggesting that the cross-compression unit in MKR and CAKR can learn more additional information, to alleviate the data sparsity problem in recommendation scenarios with high sparsity. KGCN, on the other hand, is the least effective on the Book-Crossing dataset, which is also due to the fact that data sparsity causes KGCN to easily introduce noise, degrading the model performance when aggregating neighbor information.
* Overall, the KCNR model proposed in this paper outperformed all baseline models on all three datasets. On the MovieLens-1M dataset, the AUC increased by 0.7%. On the Book-Crossing dataset, the AUC increased by 0.6%. On the Last.FM dataset, the AUC increased by 1.1%. It can be seen that KCNR utilizes the rich semantic information in the KG, and the special network structure of the KG, to enhance the embedded representation of users and items. This also alleviates the data sparsity problem of the recommendation system, to a certain extent.
* The last four rows in Table 3 show the results of KCNR using four different aggregators, when aggregating item neighbors. It can be observed that KCNR\({}_{neighbor}\) has the worst performance, because it uses only the information of the item's neighbors to represent the item, losing the original information of the item. This information is important for the item. KCNR\({}_{sum}\) and KCNR\({}_{concat}\) achieve better results than KCNR\({}_{neighbor}\), because  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Dataset** & **Parameters** \\ \hline MovieLens-1M & \(d=32,L=2,\lambda_{RS}=2\times 10^{-2},\lambda_{2}=10^{-6}\) \\ Book-Crossing & \(d=16,L=2,\lambda_{RS}=2\times 10^{-4},\lambda_{2}=10^{-6}\) \\ Last.FM & \(d=4,L=2,\lambda_{RS}=10^{-3},\lambda_{2}=10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter settings for the KCNR model.  
they consider the importance of the item's original information and neighboring entities' information. Based on KCNR\({}_{sum}\), KCNR\({}_{Bi}\) adds additional feature interaction between the item's original information and the neighboring entities' information, to supplement the information further. So it achieved better results. This also shows that the information disseminated in the KG is sensitive to the association between \(v_{\mathcal{O}(v)}^{u}\) and \(v\). It is also demonstrates that the rich semantic information in the KG can enhance user and item embedding representations and effectively improve the recommendation quality.