## 4 Experiment Evaluation
### Experimental Results
#### 4.3.1 Explanation of Experimental Methods  
To validate the method proposed in this study, comparisons between our methods with other rating prediction methods were conducted. The following is a brief description of the relevant research methods.  
1. Aspect-based Deep Learning Rating Prediction (ADLRP): The method proposed in this study. User and product reviews are analyzed, and aspect, aspect sentiment, aspect semantics, and other feature vectors are created. Through the training of the CNN, user latent factors and product latent factors are generated, and then matrix factorization is applied to predict ratings.
2. Deep Collaborative Neural Networks-CNN (DeepCoNN-CNN): DeepCoNN-CNN [10] analyzes user reviews and product reviews, and then uses a CNN to generate user latent factors and product latent factors. Finally, the matrix factorization method is used to predict the ratings.
3. Deep Collaborative Neural Networks-DNN (DeepCoNN-DNN): Similar to DeepCoNN-CNN, this method uses multilayer perceptron (MLP) to replace the CNN in DeepCoNN-CNN. This multilayer perceptron consists of two input layers (user and product reviews are input, respectively), three hidden layers, two fully connected layers (user latent factors and product latent factors are generated, respectively), and a final matrix decomposition layer for predicting ratings.  
AutoRec: AutoRec is based on collaborative filtering and Auto-Encoder to infer user ratings for unrated products, as based on the user-item matrix [48]. Auto-Encoder is an unsupervised learning algorithm and a three-layer artificial neural network with an input layer, a hidden layer, and an output layer.
* K-Nearest Neighbor (KNN): Similar to KNN-Mean, this method uses the ratings of \(K\) similar users to predict the target users' ratings [3; 50].
* K-Nearest Neighbor with Mean (KNN-Mean): Based on the collaborative filtering method, this method uses the ratings of \(K\) neighbors (i.e., similar users) and the average ratings to predict the target users' ratings for unrated products [3; 50].
* Matrix Factorization (MF): As a latent factor model, the matrix decomposition method mainly decomposes the user-item matrix into user and product latent factor matrices [16]. These two latent factor matrices are then inner-produced to calculate the predicted user-item ratings.
* Non-Negative Matrix Factorization (NMF): Similar to the MF method, NMF is a matrix decomposition method under the restriction that all elements of the matrix are non-negative [51]. As the true rating of users is non-negative, NFM ensures that the decomposed latent factors are also non-negative.
* Singular Value Decomposition (SVD++): Singular Value Decomposition (SVD) is a common matrix decomposition technique. In the collaborative filtering method, SVD decomposes the user feature matrix and item feature matrix to predict the ratings, based on the existing user-item matrix. SVD++ [47], an extension of SVD, adds users' latent factors to the user-item matrix to increase prediction accuracy.
* Aspect-aware Latent Factor Model (ALFM): The latent topics are extracted from reviews by using the aspect-aware topic model (ATM) to model users' preferences and items' features in different aspects [52]. Then, based on the latent factors, the aspect importance of a user towards an item is evaluated and linearly combined with the aspect ratings in the ALFM method to calculate the overall ratings.