# Personalized Chinese Tourism Recommendation Algorithm Based on Knowledge Graph
## 4 Experiment
### Analysis of Experimental Results  
The entity recognition experiment uses three models for comparison, namely the Hidden Markov Model (HMM), the Bi-directional Long Short-Term Memory (Bi-LSTM), and the Bi-directional Long Short-Term Memory Condition Random Field (Bi-LSTM+CRF). The Wikipedia Chinese corpus is used as the data set, and the labeling method uses the BIOES tag. In the experiment, the data are divided into a training, test and validation set by 32,620, 9320, 4660 (7:2:1). The training set is used to train the Chinese named entity recognition model, and the test set is used to measure the final effect of the model. The results of different models in Table 4.  
As shown in Table 4, compared with using only Bi-LSTM model in Chinese NER, the Bi-LSTM+CRF model effectively improves the performance, the precision of Chinese NER was increased by 0.74%, the recall was increased by 0.73%, and the F1-score was increased by 0.78% in the Wikipedia Chinese corpus dataset. According to the experimental results of the HMM model, it can be observed that the model, as a traditional statistical-based method, has a poor effect on Chinese-named entity recognition. Compared with the Chinese-named entity recognition model based on BiLSTM, the Chinese-named entity recognition model based on Bi-LSTM+CRF effectively improved the effect of the overall Chinese named entity recognition model. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Metrics** \\ **Entity** \\ \end{tabular} } & \multicolumn{3}{c}{**Precision**} & \multicolumn{3}{c}{**Recall**} & \multicolumn{3}{c}{**F1-Score**} \\ \cline{2-10}  & **HMM** & **Bi-LSTM** & **Bi-LSTM+CRF** & **HMM** & **Bi-LSTM+CRF** & **HMM** & **Bi-LSTM** & **Bi-LSTM+CRF** \\ \hline I-ORG & 0.0477 & 0.8997 & 0.9013 & 0.0464 & 0.7332 & 0.8168 & 0.0470 & 0.8080 & 0.8570 \\ B-PER & 0.0076 & 0.9247 & 0.9339 & 0.0075 & 0.8230 & 0.8723 & 0.0076 & 0.8708 & 0.9021 \\ O & 0.8678 & 0.9809 & 0.9861 & 0.8708 & 0.9921 & 0.9935 & 0.8693 & 0.9865 & 0.9898 \\ I-LOC & 0.0242 & 0.8025 & 0.8588 & 0.0251 & 0.8302 & 0.8595 & 0.0246 & 0.8616 & 0.8592 \\ B-ORG & 0.0092 & 0.8568 & 0.8500 & 0.0073 & 0.6627 & 0.7574 & 0.0081 & 0.7474 & 0.8011 \\ B-LOC & 0.0190 & 0.8069 & 0.8827 & 0.0172 & 0.8704 & 0.8767 & 0.0181 & 0.8375 & 0.8797 \\ I-PER & 0.0174 & 0.9309 & 0.9429 & 0.0183 & 0.8420 & 0.8809 & 0.0179 & 0.8842 & 0.9108 \\ Avg & 0.7720 & 0.9682 & 0.9756 & 0.7746 & 0.9689 & 0.9762 & 0.7753 & 0.9680 & 0.9758 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The experimental results of Chinese NER. It can be observed from Table 5 that the average ranking of the B-TransD model is higher than that of the TransD model, and the hit rate of top 10 is higher than that of the TransD model. It can be observed that the B-TransD model has more advantages for data with a one-to-many or many-to-one relationship, which can better retain the semantic information in knowledge graph and achieve a more accurate entity and relation vector. In this paper, the results of the B-TransD training are used to represent the knowledge graph of tourist attractions. The user interest model constructed on this basis can also better retain the structural relationship in the knowledge graph, laying the foundation for subsequent attraction recommendation. The weight calculation of the KG-CF recommendation algorithm is to fuse the weights of two parts in a certain proportion. The difference of fusion proportion has an impact on the performance of recommendation. In order to determine the best fusion proportion, this paper conducts several experiments. The experimental results demonstrate (as shown in Figure 7), whose performance is optimal when the recommendation number is 30 and the fusion ratio is 0.7. The best parameters of the knowledge representation are as follows: learning rate \(\varepsilon=0.001\), distance between positive and negative triples \(\gamma=1\), and the dimension of vector space \(d=50\).