Deep Learning-Based Recommender System for Arabic Content with Integrated Sentiment Analysis of User Reviews

Recommender systems are widely used as an information filtering technology to automatically predict and identify a set of interesting items for users based on their needs and preferences. They are widely applied in many domains, including e-commerce, social media, education, and healthcare. Recommender systems employ various filtering approaches, such as collaborative filtering, content-based filtering, and hybrid methods. Collaborative filtering is broadly categorized into memory-based and model-based approaches. Deep learning-based recommenders are a type of model-based approach that employs neural networks to capture patterns in user preferences and item features and generate accurate and personalized recommendations. In this study, we apply deep learning-based recommender systems to the Large-Scale Arabic Book Reviews Dataset (LABR) and evaluate their performance. To improve recommendation quality, we integrate sentiment analysis of user reviews using pre-trained Arabic BERT–mini and AraBERT, enabling more accurate modeling of user preferences. The results show that the combination of deep learning techniques and sentiment analysis produces more accurate recommendations, improving user satisfaction and engagement with Arabic content.