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 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 several relation types and organized as a comprehensive knowledge graph. To demonstrate the utility of the dataset, we present a systematic benchmarking study on the top-N recommendation and sequential recommendation tasks, evaluating a diverse set of representative recommendation models. Through comprehensive benchmarking, we demonstrate that recommendation performance in this domain is strongly influenced by both heterogeneous relational information and code-mixed textual metadata. These findings reveal unique challenges of Bangladeshi e-commerce ecosystems that are largely absent from existing recommendation benchmarks. 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