###### Abstract  
Recommender systems are challenged with providing accurate recommendations that meet the diverse preferences of users. The main information sources for these systems are the utility matrix and textual sources, such as item descriptions, users' reviews, and users' profiles. Incorporating diverse sources of information is a reasonable approach to improving recommendation accuracy. However, most studies primarily use the utility matrix, and when they use textual sources they do not integrate them with the utility matrix. This is due to the risk of combined information causing noise and reducing the effectiveness of good sources. To overcome this challenge, in this study we propose a novel method that utilizes the Transformer Model, a deep learning model that efficiently integrates textual and utility matrix information. The study suggests feature extraction techniques suitable for each information source and an effective integration method in the Transformer model. The experimental results indicate that the proposed model significantly improves recommendation accuracy compared to the baseline model (MLP) for the Mean Absolute Error (MAE) metric, with a reduction range of 10.79% to 31.03% for the Amazon sub-datasets. Furthermore, when compared to SVD, which is known as one of the most efficient models for recommender systems, the proposed model shows a decrease in the MAE metric by a range of 34.82% to 56.17% for the Amazon sub-datasets. Our proposed model also outperforms the graph-based model with an increase of up to 108% in Precision, a decrease of up to 65.37% in MAE, and a decrease of up to 59.24% in RMSE. Additionally, experimental results on the Movielens and Amazon datasets also demonstrate that our proposed model, which combines information from the utility matrix and textual sources, yields better results compared to using only information from the utility matrix. recommender system; deep neural network recommender system; multiview; transformer model +
Footnote †: journal: _Applied_  
using only the information contained in the utility matrix. On the other hand, the content-based approach in such studies [2; 8; 9; 10; 11] is based on the simple observation that if a person enjoys item i, they are likely to enjoy similar products. The similarity between items can be calculated by referencing the utility matrix or item profiles found in other sources. However, content-based is limited in its ability to predict ratings for new items. To overcome this limitation, recent studies have combined both approaches using ensemble learning [2; 12; 13; 14; 15; 16; 17]. Traditionally, recommender systems rely on a single source of information, such as a user's historical purchase or viewing data, to generate recommendations. However, there is growing interest in using multiple sources of information to improve the accuracy and relevance of recommendations. A proposed strategy involves employing a multimodal approach, which pertains to combining data from different sources such as Refs. [18; 19; 20; 21; 22]. Additionally, there have been some recent studies [23; 24; 25] that employ neural networks to integrate information from multiple sources for ratings prediction. Hypergraphs provide a more flexible and powerful way to model complex interactions and dependencies between users and items in recommender systems, as demonstrated by previous research [26; 27; 28]. Despite the potential benefits of using a multimodal approach for recommender systems, there has been relatively little research to date on how to effectively combine all of these different sources of information into a unified model, especially on how to combine the utility matrix with other sources. We investigated the related studies and found that studies utilizing the utility matrix [29; 30] tend to exclude other sources of information, possibly due to the effectiveness of the collaborative matrix factorization model for this particular information source.