Towards Personalized Bangla Book Recommendation: A Large-Scale Multi-Entity Book Graph Dataset

2026-02-12

AI Summary

This study addresses the scarcity of structured, large-scale public datasets for personalized book recommendation in Bengali, a low-resource language. To bridge this gap, the authors construct RokomariBG—the first large-scale Bengali book knowledge graph dataset—encompassing multiple entity types (books, users, authors, categories, publishers) and relations, along with associated textual side information. Leveraging this resource, they systematically evaluate a range of recommendation approaches, including collaborative filtering, matrix factorization, content-based features, graph neural networks, and neural two-tower retrieval models. Experimental results demonstrate that the neural retrieval model achieves the best performance (NDCG@10 = 0.204), highlighting the critical role of both multi-relational graph structure and textual features in enhancing recommendation accuracy. This work establishes a new reproducible benchmark for recommender systems research in low-resource cultural contexts.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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

Problem

• personalized recommendation
• Bangla literature
• low-resource language
• book recommendation
• dataset scarcity

Innovation

• heterogeneous knowledge graph
• low-resource language
• personalized recommendation
• graph neural networks
• benchmark dataset