Abstract
On the internet, where the number of choices of products and services is growing, users need to filter items or products to make better decisions. Recommender system is a type of information filtering system designed to provide recommendations to users based on various algorithms. These algorithms forecast the most likely products that users will buy or like based on their interests. In recent years, the number of recommender systems has increased, and famous companies have employed recommender systems to assist their users in finding the products or items that are appropriate for them. Therefore, we decided to review existing studies on recommender systems for Arabic content. Because many recommender systems focus on English content, we found a few studies in the field of recommender systems that address Arabic content. We summarize these studies based on some features, including recommender system types, domain, datasets, and if the recommender system is integrated with sentiment analysis. Finally, we discuss recommender systems with Arabic content studies, and we notice that most of these studies used sentiment analysis with recommender systems to achieve high-quality recommendations.