Semantic Search and Multimodal Personalization in Fashion E-Commerce Using Large Language Models and Vector Databases

Registration ID: IJNRD_322185

Published ID: IJNRD2603425

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Priyadharshini.M, Gokul Nath R, Shahul Hameed S, Pavithran A

fashion recommendation system, semantic vector search, large language model, sentence transformers, occasion detection, outfit assembly, multimodal personalization, color compatibility, ChromaDB, cultural fashion intelligence

Searching for clothing online is difficult for shoppers due to the limitations of conventional e-commerce shopping sites. Typically, searches on e-commerce portals utilize keywords to find product matches; therefore, if a shopper describes his/her needs with a contextual/culturally specific word/phrase, it will not be found among the product offerings. In this paper, we present a hybrid recommendation system that allows for either free-text queries to be entered into the search field or optional uploaded images to be provided by the shopper; the system's output to the shopper is a list of ranked products, in/outfit combinations, and personalized comments about styling the products with/without other products. The intent of the shopaholic is extracted using a largemodel model that translates common and culturally-specific terms into standard filter values; then, the enriched input query is entered into a 768-dimensional sentence embedding model that creates a dense vector for input to a pre-indexed product catalog using an approximate nearest-neighbour search method. If the shopper has uploaded an image of themselves, the vision-language model determines the corresponding attributes of the shopper's gender/approximate age/skin colour/physique; these attributes will be stored on the server and will automatically apply to all subsequent searches made during that session. The outfit combinations are created using a deterministic rule-based engine that authorizes colour agreement for 174 approved pairs of colours, applies outfit-specific garment slot rules and assigns an average score to each outfit combination to ensure variety in the styling offered. A total of 48 validator tests on the created outfit combinations across gender, age, and occasion produce consistent outputs each test, and the entire end-to-end time for responding to the shopper was less than two seconds in each case.

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Priyadharshini.M, Gokul Nath R, Shahul Hameed S, & Pavithran A (March-2026). Semantic Search and Multimodal Personalization in Fashion E-Commerce Using Large Language Models and Vector Databases. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(3), e167-e170. https://ijnrd.org/papers/IJNRD2603425.pdf

Volume 11 Issue 3, March-2026

Pages : e167-e170

Paper Reg. ID: IJNRD_322185

Published Paper Id: IJNRD2603425

Research Area: Other area not in list

Author Type: Indian Author

Country: Chengalpattu, Tamilnadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2603425.pdf

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