## 5 Experimental Results
### Results  
We evaluated the performance of our proposed Multiview transformer recommendation model by comparing it with the SVD, MLP, and GRU recommendation model on the MovieLens and Amazon datasets. Our experimental findings validate two hypotheses: \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **MAE** & **RMSE** & **Precision** \\ \hline \multicolumn{4}{c}{MovieLens:} \\ \hline MLP RS [three views] & 1.261 & 1.677 & 51.03\% \\ \hline GRU RS [three views] & **0.852** & **1.062** & 65.39\% \\ \hline Transformer-based RS [two views] & 0.973 & 1.230 & 67.11\% \\ \hline Transformer-based RS [three views] & 0.964 & 1.216 & **67.16\%** \\ \hline SVD RS & 1.562 & 2.293 & 47.75\% \\ \hline Amazon: Toys and Games: & & & \\ \hline MLP RS [five views] & 0.527 & 0.776 & 86.65\% \\ \hline GRU RS [five views] & 0.729 & 0.889 & 86.31\% \\ \hline Transformer-based RS [two views] & 0.570 & 1.09 & 86.85\% \\ \hline Transformer-based RS [five views] & **0.445** & **0.743** & **92.07\%** \\ \hline SVD RS & 0.844 & 1.120 & 68.06\% \\ \hline Amazon: Video and Games: & & & \\ \hline MLP RS [five views] & 0.729 & **0.999** & 73.74\% \\ \hline GRU RS [five views] & 0.941 & 1.174 & 76.89\% \\ \hline Transformer-based RS [two views] & 0.842 & 2.062 & 77.09\% \\ \hline Transformer-based RS [five views] & **0.689** & 1.471 & **83.31\%** \\ \hline SVD RS & 1.572 & 1.254 & 63.99\% \\ \hline Amazon: Electronic: & & & \\ \hline MLP RS [five views] & 0.628 & **0.914** & 83.22\% \\ \hline GRU RS [five views] & 0.78 & 1.007 & 85.80\% \\ \hline Transformer-based RS [two views] & 0.606 & 1.362 & 85.94\% \\ \hline Transformer-based RS [five views] & **0.554** & 1.195 & **87.83\%** \\ \hline SVD RS & 0.85 & 1.159 & 69.59\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The experimental results on the MovieLens and Amazon datasets* Combining information from the utility matrix and other textual features using the Transformer model results in better outcomes compared to relying solely on features generated from the utility matrix;
* The Transformer model outperforms other models (specifically, we compared it with GRU and MLP) in terms of yielding better results. Table 4 presents the experimental findings for four datasets, which include The MovieLens dataset and three Amazon sub-datasets (Toys and Games, Video and Games, and Electronic). Regarding the first hypothesis, our proposed model outperforms SVD (SVD RS), using only the information derived from the utility matrix, in all three metrics (MAE, RMSE, and Precision) on all four datasets. Regarding the second hypothesis, the Transformer model achieves the best Precision results, compared to the GRU (GRU RS) and MLP (MLP RS) models. The Transformer model also outperforms the other models in the MAE and RMSE metrics for most datasets, except for the MovieLens dataset, where the GRU model produces the best results in both the MAE and RMSE metrics. Similarly, for the Amazon-Electronic dataset, the MLP model yields the best RMSE. Specifically, during the experiments conducted on the Movielens dataset, our proposed transformer recommendation model integrated three views, which were user latent features, item latent features, and item genre features. The experimental results obtained were MAE, RMSE, and Precision metrics of 0.964, 1.216, and 67.16%, respectively. Compared to the MLP, GRU, and SVD recommendation models, the proposed transformer recommendation model exhibited the best Precision (Shown in Figure 4). Furthermore, our transformer recommendation model provided slightly better results when integrating three views than when integrating only two views. When conducting experiments on the Amazon-Toys and Games dataset, our proposed transformer recommendation model integrated five views, which were the user latent features, item latent features, item category features, item description features, and features extracted from user reviews.