面向个性化孟加拉语图书推荐：大规模异构图书图谱数据集
Towards Personalized Bangla Book Recommendation: A Large-Scale Heterogeneous Book Graph Dataset
AI总结：
针对孟加拉语文学缺乏结构化大规模公开数据集的问题，构建了RokomariBG异构图书图谱数据集，包含12.7万本书、6.3万用户等实体及多种关系，通过基准测试表明异构关系与混合文本元数据显著影响推荐性能。
摘要：
孟加拉语文学中的个性化图书推荐一直受限于缺乏结构化、大规模且公开可用的数据集。本文介绍了RokomariBG，一个大规模异构图书图谱数据集，旨在支持低资源语言环境下的个性化推荐研究。该数据集包含127,302本书、63,723个用户、16,601位作者、1,515个类别、2,757家出版社和209,602条评论，通过多种关系类型连接，并组织为综合知识图谱。为展示数据集的实用性，我们针对Top-N推荐和序列推荐任务进行了系统基准研究，评估了多种代表性推荐模型。通过全面基准测试，我们证明了该领域的推荐性能同时受异构关系信息和混合文本元数据的强烈影响。这些发现揭示了孟加拉国电商生态系统中现有推荐基准大多缺失的独特挑战。总体而言，本文为孟加拉语图书推荐研究建立了基础基准和公开可用资源，实现了可重复评估及未来对低资源文化领域推荐的研究。数据集和代码已公开于此https URL。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
