Hierarchical Book Organization for Learning-Resource Discovery using Dual-Path Graph Convolutions

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.