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        <title>On Using Arabic Language Dialects in Recommendation Systems</title>
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            <namePart type="given">Luis</namePart>
            <namePart type="family">Chiruzzo</namePart>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2025.findings-naacl.115</identifier>
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%0 Conference Proceedings
%T On Using Arabic Language Dialects in Recommendation Systems
%A Alshabanah, Abdulla
%A Annavaram, Murali
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F alshabanah-annavaram-2025-using
%X 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.
%R 10.18653/v1/2025.findings-naacl.115
%U https://aclanthology.org/2025.findings-naacl.115/
%U https://doi.org/10.18653/v1/2025.findings-naacl.115
%P 2178-2186

Markdown (Informal)

[On Using Arabic Language Dialects in Recommendation Systems](https://aclanthology.org/2025.findings-naacl.115/) (Alshabanah & Annavaram, Findings 2025)

• On Using Arabic Language Dialects in Recommendation Systems (Alshabanah & Annavaram, Findings 2025)

ACL

• Abdulla Alshabanah and Murali Annavaram. 2025. On Using Arabic Language Dialects in Recommendation Systems. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 2178–2186, Albuquerque, New Mexico. Association for Computational Linguistics.