AI Recommendation System for Enhanced Customer Experience: A Novel Image-to-Text Method

TL;DR

This paper introduces an AI-powered fashion recommendation system that uses image-to-text technology to generate detailed descriptions of clothing items, enabling more accurate and personalized product suggestions based on visual data.

Contribution

It presents a novel end-to-end pipeline that combines image captioning with fashion retrieval, improving personalization in fashion recommendations using visual interpretation.

Findings

F1-score of 0.97 for object detection on fashion images

Effective retrieval of similar fashion items based on generated descriptions

Enhanced customer engagement through personalized recommendations

Abstract

Existing fashion recommendation systems encounter difficulties in using visual data for accurate and personalized recommendations. This research describes an innovative end-to-end pipeline that uses artificial intelligence to provide fine-grained visual interpretation for fashion recommendations. When customers upload images of desired products or outfits, the system automatically generates meaningful descriptions emphasizing stylistic elements. These captions guide retrieval from a global fashion product catalogue to offer similar alternatives that fit the visual characteristics of the original image. On a dataset of over 100,000 categorized fashion photos, the pipeline was trained and evaluated. The F1-score for the object detection model was 0.97, exhibiting exact fashion object recognition capabilities optimized for recommendation. This visually aware system represents a key advancement in customer engagement through personalized fashion recommendations