## 4 Results
### Performance Measurement and Comparative Experimental Design  
Since the essence of UATF is a score prediction model, this chapter selects the general performance metrics of score prediction, mean square error (MSE), and mean absolute error (MAE) as the evaluation indicators of this experiment and comparative experiments. The calculation methods of MSE and MAE are shown in Equations (11) and (12):  
\[MSE=\frac{1}{m}\sum_{i}^{m}\left(y_{i}-\overset{\wedge}{y}_{i}\right)^{2} \tag{11}\]  
\[MAE=\frac{1}{m}\sum_{i}^{m}\left|y_{i}-\overset{\wedge}{y}_{i}\right| \tag{12}\]  
where \(\overset{\wedge}{y}_{i}\) is the predicted value, and \(y_{i}\) is the actual score value (label) in the test set. In addition to the measurement of rating prediction, users' ratings can also be divided into two evaluation types by setting a threshold: positive and negative. In this paper, considering the situation that the unit may not file in time after hiring employees, this paper defines the threshold for distinguishing positive and negative reviews as when the score is greater than or equal to four (the corresponding user has an interview opportunity but not considering whether its status is finally classified as \"Hired\") this score is \"Positive\" and \"Bad\" for the rest (when the score is less than or equal to three). In this case, the recommendation problem can be viewed as a binary classification problem and thus can be measured using the corresponding precision, recall, and \(F1\)-score:  
\[Precision=\frac{t_{p}}{\left(t_{p}+f_{p}\right)} \tag{13}\]  
\[Recall=\frac{t_{p}}{\left(t_{p}+f_{n}\right)} \tag{14}\]  
\[F1=\frac{2\times Precision\times Recall}{\left(Precision+Recall\right)} \tag{15}\]  
The \(t_{p}\) is the record that the recommendation model predicts to be positive and the actual score is also positive, \(f_{p}\) indicates the record that the recommendation model predicts to be positive but is actually negative (Type I error), and \(f_{n}\) is the record that the model predicts to be negative but is actually positive (Type II errors). Therefore, the precision rate here can also be understood as the precision rate of the model and the recall rate can be understood as the recall rate of the model. In the comparative experiment, this paper selects the following models as the comparative model of the experiment:  
Collaborative filtering model (CF): As a classic model in the recommendation model, the collaborative filtering model recommends the same item to similar users or recommends similar items to the same user by calculating the similarity between users or the similarity between recommended items. In the comparison experiment, this paper chooses the implementation of Item CF [43]. Probabilistic matrix factorization (PMF) [44]: As an important theoretical source of the method proposed in this paper, PMF is based on regularized matrix factorization and introduces a probability model for further optimization. Assuming that the feature matrices of user U and item V are both subject to a normal distribution, PMF obtains the feature matrices of U and V through the known values of the rating matrix and then uses the feature matrix to predict the unknown values in the rating matrix. SVD++ [45]: SVD++ is an improved algorithm based on matrix singular value decomposition (SVD). SVD++ introduces an implicit feedback mechanism based on SVD, using the user's historical browsing data, user historical rating data, item historical browsing data, and item historical rating data as new parameters to participate in training. Long short-term memory network (LSTM) [46]: Different from collaborative filtering-based methods, content-based recommendation methods focus on the text information left by users. This method uses the pre-trained Chinese word2 vec model to obtain the word embedding expression of the comment text and constructs a layer of bidirectional long short-term memory network (long short-term memory, LSTM) [47] for classification training.