# Research of Personalized Recommendation Technology Based on Knowledge Graphs
## 6 Discussion and Conclusions  
In this paper, we combine knowledge graphs with recommendation system. In traditional recommendation models such as collaborative filtering and matrix factorization, various behavior data analysis and modeling of users are generally used to recommend items. However, the introduction of knowledge graphs can provide a lot of additional auxiliary information for personalized recommendation systems. It can also effectively alleviating the cold start problem. Although the article only considers the construction of knowledge graphs in the movie domain, the improved recommendation algorithm proposed in this article is also applicable to many other areas, including product recommendation, music recommendation, news recommendation, etc. The algorithm has strong scalability and applicability.  
Through the analysis and exploration of related movie data, we determined the entities and relations of the knowledge graphs in the movie domain and completed the construction of the ontology database. We obtained the required movie data through crawler. We completed the construction of the knowledge graphs. Furthermore, we use Neoj graph database for storage and visual display.  
We researched and analyzed the shortcomings of the classic TransE representation learning algorithm, that is, it learns the triple data in isolation during the training process but ignores the structural data and neighbor information in the knowledge graphs. Therefore, we improved the TransE model to solve this problem. The improved model is trained by cross iteration of triple data and knowledge graph structure data. It has effectively improved the effect of learning.  
At the same time, we combined the knowledge graphs with the ranking learning and neural network, respectively, and proposed the KG-BPR model and the KG-NN model. We conducted comparative experiments on them, respectively, verifying the feasibility of the two models we proposed. At the same time, it shows that the auxiliary information of the knowledge graphs has important value in the recommendation system.  
However, because the knowledge graphs and recommendation system are particularly complex, there are some shortcomings in our work. First of all, when constructing the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Precision(\%)** \\ \hline KG-NN & 21.3 \\ MLP & 11.6 \\ NMF & 11.5 \\ SVD & 4.69 \\ ItemKNN & 2.32 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison experiment results.  
knowledge graphs, we only considered limited information, such as actors, directors, and basic movie information. Next, we will consider more useful entity data and relational data. On the other hand, the content of knowledge graphs also needs to be updated with the times. Our next step is to consider how to dynamically update the knowledge graph data in the recommendation system. In addition, because the introduction of the knowledge graphs will inevitably reduce the efficiency of the recommendation model greatly. In this paper, we only considered how to improve the effect of the recommendation model. Therefore, in the next research, we will focus on the efficiency of the model and study how to design an efficient recommendation model combined with the knowledge graphs.  
Conceptualization, Y.Z.; methodology, Y.Z.; software, Y.Z.; validation, Z.H.; formal analysis, Z.H.; investigation, Z.H.; resources, X.Y.; data curation, X.Y.; writing--original draft preparation, T.L.; visualization, T.L.; supervision, X.Y.; project administration, X.Y.; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China under Grant No. 91846303, and the Beijing Municipal Natural Science Foundation under Grand No. 4212043.  
The authors declare no conflict of interest.