TopicMap-BN: Scalable and Explainable Framework for Cross-Source Bangla News Recommendation with BanglaBERT and BERTopic
Description
With the rapid growth of online Bangla news portals, thousands of articles are published daily on similar topics, resulting in an information overload for readers. Existing recommendation systems mostly focus on personalized suggestions based on user history, whereas readers frequently desire related news on a given topic across multiple sources. This challenge is amplified by the scarcity of robust Bangla Natural Language Processing (NLP) tools and the heterogeneous structure of news content. In this regard, we introduce TopicMap-BN, a scalable and explainable topic-based framework for cross-source Bangla news recommendation system. This system integrates Bangla-specific preprocessing with neural topic modeling (BERTopic with transformer embeddings), near-duplicate detection (MinHash and SimHash), and diversityaware re-ranking (MMR, xQuAD, DPP). These components facilitate coherent story grouping, interpretable topic labels, and recommendations that maintain relevance, freshness, and diversity. The effectiveness of the proposed framework is demonstrated by experiments carried out on the Potrika corpus (approximately 665,000 articles) and live crawls from five popular news portals. As a result, the system achieved topic quality, demonstrated by an NPMI score of 0.62 and a human agreement value (κ) of 0.71. In terms of story deduplication, it secured a precision of 0.91 and an F1-score of 0.88, indicating reliable clustering of nearduplicate articles. Moreover, the framework demonstrates its ability to produce precise and well-ranked recommendations by having a precision at rank five of 0.72 and an NDCG at rank five of 0.75. Compared with classical baselines such as TF–IDF with cosine similarity, TopicMap-BN achieves substantial gains across coherence, ranking, and diversity. These findings confirm the feasibility of cross-source Bangla news recommendation and emphasize the significance of domain-specific NLP frameworks in low-resource settings.
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15525ijcsea01.pdf
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- Journal article: https://aircconline.com/ijcsea/V15N5/15525ijcsea01.pdf (URL)
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2025
References
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