Addressing Linguistic Challenges in Arabic NLP: A Comprehensive Study on Content-Based and Collaborative Filtering Techniques for

Abstract

Efficient algorithms need to detect Arabic-speaking users and provide them with content that promotes cultural exchange and knowledge acquisition. This study proposes an enhanced Arabic Article Recommendation System (AARS) grounded on semantic analysis techniques strengthened by the BERT model. The system aims to promote recommendation relevance and accuracy, a process frustrated by the complexity of the Arabic language brought about by dialectical variations and diacritical presence. To mitigate these challenges, the method takes a light normalization step for removing noise and redundant characters, followed by a morphological dialect stemmer to extract root forms and improve semantic representation. In addition, the proposed system integrates cultural, personal, and contextual dimensions—such as user interests and preferences—to make context-aware recommendations. Empirical results confirm the effectiveness of the approach in recalling and presenting relevant content and thereby enhancing user satisfaction and involvement. In addition to its theoretical significance, the system also offers economic benefits to content providers and advertisers by enabling personalized, targeted suggestions. Future work will entail model refinement and exploring advanced methods to further promote stability and adaptability in the face of future computational demands and challenges