Published on: May 2026
A COMPREHENSIVE REVIEW ON MULTILINGUAL NEWS RECOMMENDER SYSTEMS AND THEIR CHALLENGES
Siddhant
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Abstract
Multilingual News Recommender Systems (MNRS) aim to deliver personalized news across different languages, addressing the limitations of traditional English-centric recommendation approaches. With the rapid growth of regional digital news consumption, especially in linguistically diverse countries like India, multilingual recommendation has become essential for user engagement. . Recent advances in natural language processing, including multilingual transformers such as mBERT, XLM-RoBERTa, and LASER embeddings, have enabled improved cross-lingual semantic understanding. However, challenges such as low-resource languages, lack of multilingual datasets, inconsistent translation quality, semantic drift, script variation, and code-mixed text continue to restrict the effectiveness of MNRS. This review paper provides a comprehensive analysis of existing techniques, datasets, and evaluation methods used in multilingual news recommendation, highlighting their strengths and limitations. The study also identifies core research gaps and outlines future directions for building more accurate, inclusive, and real-world-ready multilingual news recommender system
How to Cite this Paper
Siddhant, (2026). A Comprehensive Review on Multilingual News Recommender Systems and their Challenges. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.429
Siddhant, . "A Comprehensive Review on Multilingual News Recommender Systems and their Challenges." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.429.
Siddhant, . "A Comprehensive Review on Multilingual News Recommender Systems and their Challenges." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.429.
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References
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- •Peer Review Type: Double-Blind Peer Review
- •Published on: May 14 2026
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