VisualSeek AI

VisualSeek AI is an intelligent fashion discovery platform that helps users find similar clothing items and personalized recommendations from large product catalogs. It combines computer vision, metadata understanding, and interactive search to translate broad style intent into precise matches. Users can upload an image or browse categories and quickly receive suggestions aligned with color, texture, shape, and use context. Unlike traditional keyword search, the system supports exploratory shopping behavior and improves relevance for visually driven decisions. Its modular architecture, practical deployment design, and user friendly interface make VisualSeek AI suitable for both experimentation and production scale retail recommendation experiences worldwide. At the core of VisualSeek AI is a multimodal embedding pipeline that maps products and user queries into a shared semantic space for high quality similarity retrieval. Deep visual encoders capture subtle style cues, while structured attributes such as category, gender, and occasion enrich ranking logic. Embeddings are stored in a vector database to enable efficient nearest neighbor search at scale. The platform also includes a fashion knowledge graph to model relationships among products, materials, and style concepts, enabling explainable recommendations. This hybrid approach balances content based matching with contextual intelligence and performs even when history is sparse or unavailable VisualSeek AI is built for usability, transparency, and continuous extension as product needs evolve over time. The web application supports authentication, image upload, recommendation presentation, and interactive exploration of fashion relationships through a knowledge graph view. This visual layer helps users and developers understand why specific items are suggested, strengthening trust in model behavior. The codebase separates model components, recommendation services, and frontend assets, which simplifies maintenance, testing, and future upgrades. Overall, VisualSeek AI demonstrates how modern AI can make fashion search more engaging, explainable, and decision oriented, creating better discovery experiences for shoppers and intelligence tools for commerce teams.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-08
DOI
https://doi.org/10.5281/zenodo.20078774
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
Field-Weighted Citation Impact
0.00
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article

VisualSeek AI

Kishore N, Arun A, Mrs Hemalatha S V, Dhanush K
Zenodo (CERN European Organization for Nuclear Research)
Generative Adversarial Networks and Image Synthesis
article

VisualSeek AI

Kishore N, Arun A, Mrs Hemalatha S V, Dhanush K
article en

Abstract

VisualSeek AI is an intelligent fashion discovery platform that helps users find similar clothing items and personalized recommendations from large product catalogs. It combines computer vision, metadata understanding, and interactive search to translate broad style intent into precise matches. Users can upload an image or browse categories and quickly receive suggestions aligned with color, texture, shape, and use context. Unlike traditional keyword search, the system supports exploratory shopping behavior and improves relevance for visually driven decisions. Its modular architecture, practical deployment design, and user friendly interface make VisualSeek AI suitable for both experimentation and production scale retail recommendation experiences worldwide. At the core of VisualSeek AI is a multimodal embedding pipeline that maps products and user queries into a shared semantic space for high quality similarity retrieval. Deep visual encoders capture subtle style cues, while structured attributes such as category, gender, and occasion enrich ranking logic. Embeddings are stored in a vector database to enable efficient nearest neighbor search at scale. The platform also includes a fashion knowledge graph to model relationships among products, materials, and style concepts, enabling explainable recommendations. This hybrid approach balances content based matching with contextual intelligence and performs even when history is sparse or unavailable VisualSeek AI is built for usability, transparency, and continuous extension as product needs evolve over time. The web application supports authentication, image upload, recommendation presentation, and interactive exploration of fashion relationships through a knowledge graph view. This visual layer helps users and developers understand why specific items are suggested, strengthening trust in model behavior. The codebase separates model components, recommendation services, and frontend assets, which simplifies maintenance, testing, and future upgrades. Overall, VisualSeek AI demonstrates how modern AI can make fashion search more engaging, explainable, and decision oriented, creating better discovery experiences for shoppers and intelligence tools for commerce teams.

Zenodo (CERN European Organization for Nuclear Research)
Gender equality
Openalex Percentile: Top 4%
Generative Adversarial Networks and Image Synthesis
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