Semantic Hash-Based Retrieval Framework for Explainable Visual Recommendation Systems

Authors

  • Cesar Waga Department of Computer Science, Colorado State University, Fort Collins, CO, USA.
  • Quentin D. Bell School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, USA.
  • Malcolm A. Carr Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, USA.
  • Adrian Terry Department of Computer Science, Binghamton University, Binghamton, NY, USA.

Keywords:

Semantic hashing, visual recommendation, explainability, retrieval systems, binary codes, system architecture, fairness, governance

Abstract

Visual recommendation systems have become critical components in e-commerce, media streaming, and social platforms, demanding both retrieval efficiency and user trust through transparency. Traditional deep learning-based recommenders excel in accuracy but often operate as opaque black boxes, raising significant concerns around fairness, accountability, and user acceptance. Simultaneously, the explosive growth of multimedia databases requires retrieval mechanisms that scale while preserving semantic fidelity. This paper presents a comprehensive system-level investigation into a semantic hash-based retrieval framework that unifies efficient approximate nearest neighbor search with explainable reasoning for visual recommendations. We examine the architectural foundations that couple deep hashing encoders, binary code index structures, and explanation generation modules into a cohesive socio-technical infrastructure. The discussion focuses on structural trade-offs among inference latency, storage footprint, explanation granularity, and environmental sustainability. We embed the framework within broader governance and policy contexts, analyzing how semantic hashing can facilitate compliance with data protection regulations and fairness mandates by enabling interpretable audit trails. Deployment considerations are explored across edge-cloud continuums, addressing robustness to distributional shift, adversarial perturbations, and long-tail retrieval dynamics. The paper further proposes design principles that balance business metrics with ethical imperatives, emphasizing modularity, continuous fairness monitoring, and energy-aware serving strategies. By synthesizing insights from systems engineering, human-computer interaction, and algorithmic fairness, this work provides a forward-looking blueprint for building next-generation visual recommendation infrastructures that are simultaneously fast, interpretable, and responsible.

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Published

2026-06-01

How to Cite

Cesar Waga, Quentin D. Bell, Malcolm A. Carr, & Adrian Terry. (2026). Semantic Hash-Based Retrieval Framework for Explainable Visual Recommendation Systems. International Journal of Clinical and Translational Medicine, 1(1). Retrieved from https://ijctmed.org/index.php/home/article/view/143

Issue

Vol. 1 No. 1 (2026): International Journal of Clinical and Translational Medicine

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