Recommender System for Arabic Content Using Sentiment Analysis of User Reviews

Recommender systems are used as effective information-filtering techniques to automatically predict and identify sets of interesting items for users based on their preferences. Recently, there have been increasing efforts to use sentiment analysis of user reviews to improve the recommendations of recommender systems. Previous studies show the advantage of integrating sentiment analysis with recommender systems to enhance the quality of recommendations and user experience. However, limited research has been focused on recommender systems for Arabic content. This study, therefore, sets out to improve Arabic recommendation systems and investigate the impact of using sentiment analysis of user reviews on the quality of recommendations. We propose two collaborative filtering recommender systems for Arabic content: the first depends on users' ratings, and the second uses sentiment analysis of users' reviews to enhance the recommendations. These proposed models were tested using the Large-Scale Arabic Book Reviews dataset. Our results show that, when the user review sentiment analysis is combined with recommender systems, the quality of the recommendations is improved. The best model was the singular value decomposition (SVD) with the Arabic BERT-mini model, which yielded minimum errors in terms of RMSE and MAE values and outperformed the performance of other previous studies in the literature.