## 4 Experiment Evaluation
### Experimental Results
#### 4.3.5 Comparison of Rating Prediction Methods  
This experiment compared the ADLRP method, as proposed in this study, with three different types of rating prediction methods, including:  
1. Matrix factorization methods: MF, NMF, SVD++, and ALFM.
2. Nearest neighbor methods: KNN and KNN-Mean.
3. Deep learning methods: AutoRec, DeepCoNN-DNN, DeepCoNN-CNN, and ADLRP.  
The description of each method is described in Section 4.3.1. In the matrix factorization methods, the number of implied factors of the three MF-related methods was set in the range of 20 to 100, in increments of 10. The results show that the optimal number of implied factors is 20 for all of MF, NMF, and SVD++. In the nearest neighbor methods, the neighbor number \(K\) of the KNN and KNN-Mean methods was set in the range of 20 to 100, in increments of 10 in each experiment. The number of neighbors with the best prediction result was selected as the best parameter. According to the experimental results, the best \(K\) was 20 for KNN and 80 for KNN-Mean. In the deep learning method, the parameters of the AutoRec method were set as follows: the number of neurons: 500, optimizer: _Adam_, and learning rate: 0.001. Both DeepCoNN-DNN and DeepCoNN-CNN use GloVe to convert reviews into word vectors. DeepCoNN-DNN consists of three hidden layers and two fully connected layers, with 200 neurons in each hidden layer, _Adam_ as the optimizer, and MSE as the loss function. DeepCoNN-CNN has 2 filters, a convolutional kernel size of 10, and 6 strides, with _tanH_ and _ReLu_ being the activation functions in the convolutional and fully connected layers, respectively, _Adam_ being the optimizer, and MSE being the loss function. The parameters of the ADLRP method, as proposed in this study, are described  
Figure 9: Comparison of user-based and item-based ADLRP methods.  
in Section 4.3.2. In this experiment, the best parameters of each method were selected to compare the rating prediction results, as shown in Figures 10 and 11.  
Among the MF-based methods, ALFM has the best prediction ability. In addition to the original rating matrix, ALFM extracts the latent topics from reviews and predicts ratings based on the combination of aspect importance and aspect ratings. The latent topics and aspect importance help to effectively enhance prediction accuracy. In contrast, the traditional MF-based method only uses the rating matrix for rating prediction, and its prediction accuracy is the worst among all the methods. In the KNN-based methods, the prediction accuracy of the KNN-Mean is slightly better than that of the traditional KNN.  
In the deep learning-related methods, the prediction accuracy is AutoRec < DeepCoNN DNN < DeepCoNN CNN < ADLRP. AutoRec only uses the rating data to make predictions and does not analyze the review content; thus, compared with other deep learning-related methods, the prediction accuracy of AutoRec is unsatisfactory. Except for SVD++, AutoRec outperforms the MF-based and KNN-based methods, which only use rating data for predictions. Both DeepCoNN DNN and DeepCoNN CNN predict ratings based only on user and product features, but DeepCoNN CNN outperforms DeepCoNN DNN; this also indicates that a CNN can better improve the accuracy of rating predictions compared  
Figure 11: Comparison of RMSE in all methods.  
Figure 10: Comparison of MAE in all methods.  
to a deep neural network (DNN). The proposed method, ADLRP, can produce the best prediction results, as it analyzes the implicit aspects, sentiment, and semantics in reviews, which helps to build more accurate user preference features and product evaluation features. CNN can effectively improve the accuracy of rating prediction. In general, the prediction accuracy of a method that analyzes the implicit features of the review text is better than that of a method that only uses the rating data. Moreover, deep learning-related methods also help to improve rating prediction accuracy.