New Large-Scale Bangla Book Recommendation Dataset Released for Low-Resource Language Research
Researchers have introduced RokomariBG, a large-scale heterogeneous book graph dataset containing over 127,000 books and 63,000 users to advance personalized book recommendation research in Bangla, a low-resource language. The dataset includes structured information on authors, publishers, categories, and user reviews organized as a knowledge graph, addressing a significant gap in recommendation system research for non-English languages. This resource enables reproducible benchmarking and future studies on recommendation algorithms in underrepresented cultural and linguistic domains.
Researchers have released RokomariBG, a comprehensive heterogeneous book graph dataset designed to support personalized book recommendation research in Bangla literature. The dataset comprises 127,302 books, 63,723 users, 16,601 authors, 1,515 categories, 2,757 publishers, and 209,602 reviews, all connected through multiple relation types and organized as a knowledge graph. The authors conducted systematic benchmarking on top-N recommendation and sequential recommendation tasks using diverse representative models, finding that recommendation performance is strongly influenced by both heterogeneous relational information and code-mixed textual metadata. The study reveals unique challenges specific to Bangladeshi e-commerce ecosystems that are largely absent from existing recommendation benchmarks. The dataset and accompanying code have been made publicly available to enable reproducible evaluation and future research on recommendation systems in low-resource cultural domains.
What's missing
The study does not discuss potential limitations regarding data privacy, user consent mechanisms, or how personally identifiable information was handled in the dataset creation process. Additionally, the paper does not address potential biases in the dataset (e.g., representation of different user demographics, author visibility disparities) or discuss generalizability of findings to other low-resource languages.
What different sources said
- arXiv cs.LGCenter
Towards Personalized Bangla Book Recommendation: A Large-Scale Heterogeneous Book Graph Dataset
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