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This research introduces HiGeMine, a novel framework that applies hierarchical graph convolutions to improve semantic organization of digital learning resources by distinguishing between authoritative blurbs and subjective reviews. By reformulating genre prediction as coarse-to-fine reasoning over heterogeneous textual evidence, the method addresses limitations in existing flat classification approaches while leveraging pretrained language models for robust inference. The work establishes a new benchmark and demonstrates superior performance against standard LLMs, offering a scalable solution for managing large-scale textual repositories in educational contexts.
Hierarchical Book Organization for Learning-Resource Discovery using Dual-Path Graph Convolutions
arXiv:2512.21076v2 Announce Type: replace-cross Abstract: The growing availability of books and textual materials in digital learning environments necessitates reliable semantic organization to support scalable resource management and discovery. However, existing book classification approaches typically formulate genre prediction as a flat classification problem, overlooking both the hierarchical organization of literary categories and the semantic discrepancy between authoritative book descriptions and subjective crowd-sourced reviews. We propose {\titleabbr}, a hierarchical book classification framework for structured learning-resource organization that reformulates genre prediction as coarse-to-fine semantic reasoning over heterogeneous textual evidence. HiGeMine first performs blurb-guided semantic refinement to preserve semantically consistent reviews while suppressing noisy and genre-irrelevant interpretations. It then performs semantic-role-separated hierarchical graph reasoning through independent propagation branches for blurbs and reviews, enabling separate modeling of narrative and interpretive semantics during hierarchical inference. A coarse-grained level-1 classifier first distinguishes fiction from non-fiction, followed by domain-specialized level-2 multi-label classifiers for fine-grained genre prediction. HiGeMine captures relationships among fine-grained resource categories through a structured label co-occurrence graph and genre-conditioned semantic representations. To facilitate systematic evaluation, we curate a new hierarchical multi-label Goodreads benchmark containing paired blurbs and reviews. Experiments against hierarchical classifiers, graph-based approaches, pretrained LMs, and LLMs demonstrate the effectiveness of HiGeMine for reliable hierarchical book classification, thereby providing a scalable foundation for organizing and discovering textual resources in digital learning environments.
Abstract:The growing availability of books and textual materials in digital learning environments necessitates reliable semantic organization to support scalable resource management and discovery. However, existing book classification approaches typically formulate genre prediction as a flat classification problem, overlooking both the hierarchical organization of literary categories and the semantic discrepancy between authoritative book descriptions and subjective crowd-sourced reviews. We propose {\titleabbr}, a hierarchical book classification framework for structured learning-resource organization that reformulates genre prediction as coarse-to-fine semantic reasoning over heterogeneous textual evidence. HiGeMine first performs blurb-guided semantic refinement to preserve semantically consistent reviews while suppressing noisy and genre-irrelevant interpretations. It then performs semantic-role-separated hierarchical graph reasoning through independent propagation branches for blurbs and reviews, enabling separate modeling of narrative and interpretive semantics during hierarchical inference. A coarse-grained level-1 classifier first distinguishes fiction from non-fiction, followed by domain-specialized level-2 multi-label classifiers for fine-grained genre prediction. HiGeMine captures relationships among fine-grained resource categories through a structured label co-occurrence graph and genre-conditioned semantic representations. To facilitate systematic evaluation, we curate a new hierarchical multi-label Goodreads benchmark containing paired blurbs and reviews. Experiments against hierarchical classifiers, graph-based approaches, pretrained LMs, and LLMs demonstrate the effectiveness of HiGeMine for reliable hierarchical book classification, thereby providing a scalable foundation for organizing and discovering textual resources in digital learning environments.
Information Retrieval (cs.IR); Machine Learning (cs.LG); Multimedia (cs.MM)
arXiv:2512.21076 [cs.IR]
(or arXiv:2512.21076v2 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2512.21076 Focus to learn more arXiv-issued DOI via DataCite
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