Adaptive Data-Resilient Multi-Modal Hierarchical Multi-Label Book Genre Identification
Utsav Kumar Nareti, Soumi Chattopadhyay, Prolay Mallick, Suraj Kumar, Chandranath Adak, Ayush Vikas Daga, Adarsh Wase, Arjab Roy

TL;DR
This paper introduces IMAGINE, a multi-modal, hierarchical, and adaptive framework for fine-grained book genre identification that remains effective despite incomplete or noisy data across modalities.
Contribution
It presents a novel adaptive multi-modal network that leverages hierarchical genre taxonomy and handles missing data, improving genre classification accuracy.
Findings
IMAGINE outperforms baseline models in incomplete data scenarios.
The hierarchical approach captures inter-genre relationships effectively.
The framework maintains high accuracy even with missing modalities.
Abstract
Identifying fine-grained book genres is essential for enhancing user experience through efficient discovery, personalized recommendations, and improved reader engagement. At the same time, it provides publishers and marketers with valuable insights into consumer preferences and emerging market trends. While traditional genre classification methods predominantly rely on textual reviews or content analysis, the integration of additional modalities, such as book covers, blurbs, and metadata, offers richer contextual cues. However, the effectiveness of such multi-modal systems is often hindered by incomplete, noisy, or missing data across modalities. To address this, we propose IMAGINE (Intelligent Multi-modal Adaptive Genre Identification NEtwork), a framework designed to leverage multi-modal data while remaining robust to missing or unreliable information. IMAGINE learns modality-specific…
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Taxonomy
TopicsAuthorship Attribution and Profiling · Text Readability and Simplification · Topic Modeling
