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The authors introduce RokomariBG, a large-scale, multi-entity heterogeneous book graph dataset for personalized Bangla book recommendation, addressing the lack of resources in this low-resource language setting. They construct a knowledge graph comprising books, users, authors, categories, publishers, and reviews connected through eight relation types. Through benchmarking experiments on Top-N recommendation using collaborative filtering, matrix factorization, content-based methods, graph neural networks, and neural retrieval models, they demonstrate the dataset's utility and the importance of leveraging multi-relational structure and textual side information, achieving an NDCG@10 of 0.204 with neural retrieval models.
Unlock personalized book recommendations in Bangla with RokomariBG, a new large-scale dataset that reveals how neural retrieval models outperform traditional methods by leveraging multi-relational structure and textual data.
Personalized book recommendation in Bangla literature has been constrained by the lack of structured, large-scale, and publicly available datasets. This work introduces RokomariBG, a large-scale, multi-entity heterogeneous book graph dataset designed to support research on personalized recommendation in a low-resource language setting. The dataset comprises 127,302 books, 63,723 users, 16,601 authors, 1,515 categories, 2,757 publishers, and 209,602 reviews, connected through eight relation types and organized as a comprehensive knowledge graph. To demonstrate the utility of the dataset, we provide a systematic benchmarking study on the Top-N recommendation task, evaluating a diverse set of representative recommendation models, including classical collaborative filtering methods, matrix factorization models, content-based approaches, graph neural networks, a hybrid matrix factorization model with side information, and a neural two-tower retrieval architecture. The benchmarking results highlight the importance of leveraging multi-relational structure and textual side information, with neural retrieval models achieving the strongest performance (NDCG@10 = 0.204). Overall, this work establishes a foundational benchmark and a publicly available resource for Bangla book recommendation research, enabling reproducible evaluation and future studies on recommendation in low-resource cultural domains. The dataset and code are publicly available at https://github.com/backlashblitz/Bangla-Book-Recommendation-Dataset