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

arXiv: https://arxiv.org/abs/2311.09624 · HTML: https://mlsanity.com/paper/2311.09624/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://mlsanity.com/paper/2004.06165/) (2020)
- [Contrastive language and vision learning of general fashion concepts](https://mlsanity.com/paper/2204.03972/) (2022)
- [Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction](https://mlsanity.com/paper/1709.09426/) (2017)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Large Scale Visual Recommendations From Street Fashion Images](https://mlsanity.com/paper/1401.1778/) (2014)
- [Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks](https://mlsanity.com/paper/2307.11468/) (2023)
- [An Intelligent Resource Reservation for Crowdsourced Live Video Streaming Applications in Geo-Distributed Cloud Environment](https://mlsanity.com/paper/2106.02420/) (2021)

## Built on by (most cited first, 1 of 1)

- [Automatic Generation of Fashion Images using Prompting in Generative Machine Learning Models](https://mlsanity.com/paper/2407.14944/) (2024)

## Abstract (arXiv)

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