## 5 Experimental Results
### Experimental Setups for Models  
The goal of our research is to demonstrate that combining different views using the Transformer model is more efficient than using only one view, such as the Utility Matrix, and is better than using the SVD model for the Utility Matrix. It is important to empirically prove this hypothesis using the same established configuration. Therefore, we choose the test configuration without paying full attention to choosing the configuration that gives \begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Dataset** & **Trained Ratings** & **Validated Ratings** & **Tested Ratings** \\ \hline MovieLens & 62,211 & 3110 & 12,442 \\ \hline Amazon-Toys and Games & 54,482 & 2724 & 10,896 \\ \hline Amazon-Electronic & 64,378 & 3218 & 12,876 \\ \hline Amazon-Video and Games & 79,016 & 3950 & 15,803 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Setting up the data for the experiments. the best results. The parameter values were chosen based on previous studies and were experimented with several different values to choose the best configuration within that set. For the magnitude of the attribute vectors, we have set the following:  
* Because we use BERT for representing user reviews in our dataset, the magnitude of the resulting attribute vector is 768 dimensions, which is equal to the magnitude of the output vector of the BERT-base model;
* To represent the latent attributes of users and items using the factorization matrix method, we tested different magnitudes of the latent feature vector, including 20, 50, and 100. After evaluating the performance on the validation set, we selected the magnitude of 50 as the best choice;
* When using the TF-IDF measure for attribute representation, we utilized the set of all tokens in our data. Therefore, the corresponding magnitude of these representation vectors will be equal to the number of keywords. Given that \(fu_{1}\), \(fi_{1}\), \(fi_{2}\), \(fu_{2}\), and \(fi_{3}\) represent user latent features, item latent features, item genre/category features, user review features, and item description features, respectively. Our proposed Multiview Transformer recommendation model is set up with dimensional parameters for each input feature vector, as shown in Table 3.  
* For the Movielens dataset, the dimensions of \(fu_{1}\), \(fi_{1}\), and \(fi_{2}\) are 50, 50, and 20, respectively;
* For the Amazon-Toys and Games dataset, the dimensions of \(fu_{1}\), \(fi_{1}\), \(fi_{2}\), \(fu_{2}\), and \(fi_{3}\) are 50, 50, 321, 768, and 768, respectively;
* For the Amazon-Electronics dataset, the dimensions of \(fu_{1}\), \(fi_{1}\), \(fi_{2}\), \(fu_{2}\), and \(fi_{3}\) are 50, 50, 944, 768, and 768, respectively;
* For the Amazon-Video and Games dataset, the dimensions of \(fu_{1}\), \(fi_{1}\), \(fi_{2}\), \(fu_{2}\), and \(fi_{3}\) are 50, 50, 16,978, 768, and 768, respectively. For the Multiview selection, we designed different experiments to investigate the effect of using different views of the data, namely:  
* Experiment with data represented by 2 views, representing users and items, generated from the utility matrix;
* Experiment with data represented by all views. For MovieLens data, there are 3 views in all, of which 2 are from the utility matrix and 1 view is Genre; For Amazon data, there are 5 views in all, of which 2 are from the utility matrix and 3 are from user review, item description, and item category. For the ratings prediction model, we have designed the following models:
* We have conducted experiments with different configurations of the Transformer model in our proposed approach, including varying numbers of layers (2, 4, or 6) and hidden state values (20 and 50). After evaluating their performance on the validation set, we selected the optimal configuration of (Layers = 4, Hidden state = 20, Heads = 4);
* We have also designed two baseline models, namely the MLP (Feedforward Neural Network) and the Gated Recurrent Units (GRU), to demonstrate that the Transformer model performs better;