## 6 Conclusions  
In this study, we propose a new multimodal approach (i.e., multiview fusion) for the recommender systems problem. Our method integrates various information sources, including the utility matrix and textual sources, which previous studies have not been able to solve. Our model is based on the Transformer Model and feature extraction methods for each information source. Our experimental results show that the proposed model performs better than both the SVD method and the baseline model (MLP), which only use user and item representation based on the utility matrix. In summary, the experimental results show that our proposed model achieved higher recommendation accuracy compared to the baseline model (MLP) \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dataset** & **Methods** & **Number of Views** & **MAE** & **RMSE** & **Precision** \\ \hline \multirow{3}{*}{MovieLens} & Graph & Three views & 1.401 & 1.908 & 60.43\% \\ \cline{2-6}  & Our proposed & Three views & 0.964 & 1.216 & 67.16\% \\ \hline \multirow{2}{*}{Amazon: Toys and Games} & Graph & Five views & 1.285 & 1.823 & 60.30\% \\ \cline{2-6}  & Our proposed & Five views & 0.445 & 0.743 & 92.07\% \\ \hline \multirow{2}{*}{Amazon: Video and Games} & Graph & Five views & 1.305 & 1.742 & 56.01\% \\ \cline{2-6}  & Our proposed & Five views & 0.689 & 1.471 & 83.31\% \\ \hline \multirow{2}{*}{Amazon: Electronic} & Graph & Five views & 1.542 & 1.863 & 42.25\% \\ \cline{2-6}  & Our proposed & Five views & 0.554 & 1.195 & 87.83\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparison between our proposed model and a graph-based RS. Figure 7: The experimental results for the MAE, Precision with the Amazon-Electronic dataset. in terms of Precision, with increases ranging from 6.03% to 35.48% for the Amazon and MovieLens datasets. The proposed model also demonstrated improved accuracy in terms of MAE, with reductions ranging from 10.79% to 31.03% for the Amazon datasets. Furthermore, compared to SVD, known as one of the most effective models for RSs, the proposed model showed an increase in Precision ranging from 26.21% to 40.65%, and in MAE ranging from 34.82% to 56.17% for the Amazon datasets. Moreover, the experimental results obtained from the MovieLens and Amazon datasets provide evidence that our proposed model, which integrates information from both the utility matrix and textual sources, outperforms using only the information from the utility matrix in terms of recommendation accuracy. Moreover, we emphasize that performance may vary based on data characteristics, and we cannot assert that using multiple views will always enhance performance. The quality of additional data may also impact the model's overall performance. We have performed an analysis that illustrates the superiority of our proposed model over the SVD approach in all four datasets, with better performance observed in three out of the four datasets. Furthermore, we conducted an analysis that demonstrates the superiority of our proposed model over the SVD approach in all four datasets, with better performance observed in three out of the four datasets. Additionally, our recommendation model significantly improves the accuracy of recommendations compared to the graph-based recommendation model. It achieves an increase of up to 108% in the Precision metric, a decrease of up to 65.37% in the MAE metric, and a decrease of up to 59.24% in the RMSE metric. In future work, we will continue to expand by incorporating new sources of information and further improve the Transformer-based model to make the integration of multiple views more efficient. Conceptualization, T.-L.H. ; Data curation, T.-L.H. ; Formal analysis, T.-L.H. and A.-C.L. ; Investigation, T.-L.H. and A.-C.L. ; Methodology, T.-L.H., A.-C.L. and D.-H.V. ; Supervision, A.-C.L. ; Writing--original draft, T.-L.H. ; Writing--review and editing, T.-L.H. and A.-C.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.