improvement of recommender systems based on reviews using neural attention mechanism and lstm

Recommender systems on online sales sites usually collect users' opinions about the products in two ways namely rating and reviews. in the reviews content, there is a lot of information that is less commonly used and they also differ in importance. this paper presents a review-based recommender system using a deep learning approach and the attention mechanism. this model consists of two parallel networks, one is trained to model the users and the other one to model the items. each of these networks comprises the four phases of: preprocessing, word embedding, feature extraction, and the attention mechanism. then, in the last layer, the two networks are merged and with matrix factorization method, the final estimated rating is obtained. simulation results of the proposed model are compared with two other models, namely deepconn and narre, and show that the proposed model performs better in terms of rmse and mae evaluation metrics.