# Knowledge Graph Double Interaction Graph Neural Network for Recommendation Algorithm
## 6 Conclusions  
In this paper, we propose an end-to-end recommendation model of the knowledge graph double interaction graph neural network, which effectively alleviates the data-sparsity problem of traditional recommendation systems. The model performs multi-hop message transmission made on the knowledge graph, which gathers the features on the central node's neighbors to the central node. The central node feature is an integration of the multi-hop neighbor features and the central node's own features; it concatenates the integrated user information with the entity's own information and neighborhood information, and then conducts double interaction between the two features. Furthermore, a label propagation algorithm is proposed to learn edge weights and train transformation matrix. Experiments were conducted on the publicly available datasets MoviesLens-1M and Book-Crossing, comparing four baseline models using AUC values and F1 values as evaluation metrics, and the experiments showed that KGDI showed better results and validated the effectiveness of KGDI. There are various types of relations, and algorithms can be mined to learn the hidden states of edges to improve the performance of recommender systems. Studying interpretable recommender systems is also a promising direction.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(K\) & **2** & **4** & **8** & **16** & **32** & **64** \\ \hline MoviesLens-1M & 0.905 & 0.900 & 0.905 & 0.892 & 0.901 & 0.898 \\ Book-Crossing & 0.697 & 0.696 & 0.687 & 0.685 & 0.689 & 0.694 \\ \hline \hline \end{tabular}
\end{table}
Table 6: AUC result of KGDI with different neighbor sampling size K.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**MovieLens-1M**} & \multicolumn{2}{c}{**Book-Crossing**} \\ \cline{2-5}  & **AUC** & **F1** & **AUC** & **F1** \\ \hline KGDI & 0.905 & 0.835 & 0.698 & 0.640 \\ KGDI-labels & 0.900 & 0.828 & 0.692 & 0.618 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effect of Label Propagation Algorithms.  
**Author Contributions:** Conceptualization, S.K.; methodology, S.K. and L.S.; software, S.K.; validation, L.S.; resources, S.K.; data curation, S.K.; writing--original draft preparation, S.K.; writing--review and editing, S.K., L.S. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the key research and development project of the Ministry of Science and Technology [No. 2017 YFE0135700].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets used in this study are publicly accessible. The MoviesLens-1M dataset is available at: [[http://grouplens.org/datasets/movielens/](http://grouplens.org/datasets/movielens/)]([http://grouplens.org/datasets/movielens/](http://grouplens.org/datasets/movielens/)), accessed on March 2003. The Book-Crossing dataset is available at: [[http://www2.informatik.uni-freiburg.de/~cziegler/BX/](http://www2.informatik.uni-freiburg.de/~cziegler/BX/)]([http://www2.informatik.uni-freiburg.de/~cziegler/BX/](http://www2.informatik.uni-freiburg.de/~cziegler/BX/)), accessed on September 2004.  
**Acknowledgments:** We sincerely thank the editors and the reviewers for their valuable comments in improving this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.