## 4 Results
### Discussion  
Based on the above results, in order to evaluate the validity of the model and the reliability of the experiment, further analysis and discussion should be conducted on the following contents:  
In the experimental results, neither the content-based recommendation method (LSTM) nor the content-related hybrid recommendation method (RTTF) performed well on some general datasets. According to a retrospective analysis of the data, an important reason for its significant performance drop lies in the sparsity of the review data. This kind of sparsity can be regarded as two parts: the sparseness of comment data quantity and the sparseness of comment language expression. First, content-based recommendation needs to rely on review text. In reality, unlike e-commerce platforms where user reviews are part of the tight process of product purchases, the recruitment process of users on online job recruitment platforms is often much longer than the process of product purchases, and there are complex and uncertain interactions between users and recruiting units. Therefore, the willingness of users to comment has dropped significantly (Figure 3, the data comes from the Amazon Book Category Dataset, the BIT Evaluation Database [49], and the data of the rezhao platform used in this paper), resulting in a decrease in the number of comments. In addition, when users are not willing to evaluate, they are more inclined to use short text length and single language expression for evaluation. Although there are quite a few meaningful comments, there are often still comments that are considered unconstructive. It is foreseeable that when users are required to provide comments by means of rewards or coercion, the problem of sparse language expression will be more serious (Figure 4 data comes from the Douban Movie Review Dataset [50], BIT Evaluation Dataset [49], and rezhao platform dataset used in this paper). Therefore, the experiments in this paper show to a certain extent that the use of content recommendation in the current recruitment process does not make the recommendation system significantly benefit from problems such as cold start or sparse scoring. Figure 3: Comparison of the number of ratings with and without text reviews on different platforms. In order to further confirm the effectiveness of the model and evaluate the impact of different parameters and implementation details on performance, this paper further tests the performance of the model on the test set by adjusting different parameters. First of all, for the number of attention levels, the experiment compares the score prediction performance of the model under the condition of different attention-level number settings from 1 to 10 layers, and the relevant results are shown in Figure 5. It can be seen that the model achieves the best results when the number of attention levels is six. A reasonable guess for this situation is that when the number of attention levels is too small, the model cannot effectively distinguish different attentions, resulting in a decline in model performance; and when there are too many attention levels, the score tensor will be too sparse and cause serious problems, including the overfitting problem. Figure 4: Comparison of the number of words in text comments in different platform datasets. Figure 5: The effect of different attention levels on the average absolute error and mean square error of the model on the test set. Similarly, using different \(u_{i}\), \(v_{j}\), and \(a_{k}\) vector lengths also has an impact on the performance of the model. Figure 6 shows the comparison of MAE and MSE of the model on the test set under different vector lengths (\(length\in N|length=5x,x=1,2,3,4,5,6,7\)). Experiments show that the model performance reaches its best when the vector length value is 20. The results in Figures 5 and 6 show that the essence of the impact of this parameter setting is the adjustment between underfitting and overfitting of the model.