While natural language processing (NLP) techniques have been applied to user reviews in recommendation systems, the potential of leveraging Arabic dialects in this context remains unexplored. Arabic is spoken by over 420 million people, with significant dialectal variation across regions. These dialects, often classified as low-resource languages, present both challenges and opportunities for machine learning applications. This paper represents the first attempt to incorporate Arabic dialects as a signal in recommendation systems. We explore both explicit and implicit approaches for integrating Arabic dialect information from user reviews, demonstrating its impact on improving recommendation performance. Our findings highlight the potential for leveraging dialectal diversity in Arabic to enhance recommendation systems and encourage further research at the intersection of NLP and recommendation systems within the Arab multicultural world.

Recommendation systems play a critical role in shaping user experiences and access to digital content. However, these systems can exhibit unfair behavior when their performance varies across user groups, especially in linguistically diverse populations. Recent advances in NLP have enabled the identification of user dialects, allowing for more granular analysis of such disparities. In this work, we investigate fairness disparities in recommendation quality among Arabic-speaking users, a population whose dialectal diversity is underrepresented in recommendation system research. By uncovering performance gaps across dialectal variation, we highlight the intersection of NLP and recommendation system and underscore the broader social impact of NLP. Our findings emphasize the importance of interdisciplinary approaches in building fair recommendation systems, particularly for global and local platforms serving diverse Arabic-speaking communities. The source code is available at https://github.com/alshabae/FairArRecSys.