SholaAkinyemi01/-Fashion-Discovery-in-African-E-Commerce
Record type:
project
Creator:
Sho
Host:
# Fashion Product Recommendation System Discovery-in-African-E-Commerce
## Project Overview
The **Fashion Product Recommendation System** is an AI/ML-based content recommendation project designed to help users discover fashion products similar to a selected product.
The system combines:
- **Product images** for visual characteristics.
- **Product metadata** including gender, category, sub-category, article type, colour, season, usage, and product name.
The features are normalized, combined, and compared using **cosine similarity** to generate Top-K recommendations.
## Problem Statement
Large fashion catalogues make product discovery difficult. This project builds a recommendation system that takes a fashion product as input, analyzes its image and metadata, ranks similar catalogue products, and returns the Top-K recommendations.
Because the available dataset does not provide a complete real-world user-item interaction history, the project uses a **content-based recommendation approach** rather than relying on collaborative filtering.
## Objectives
- Build an end-to-end reproducible recommendation pipeline.
- Validate and preprocess product data and images.
- Extract visual image features.
- Encode structured product metadata.
- Combine image and metadata representations.
- Generate Top-K recommendations using cosine similarity.
- Evaluate recommendation quality.
- Analyze errors, limitations, ethics, and deployment requirements.
## End-to-End Workflow
```text
Raw Dataset
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v
Data Quality Checks
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+-------------------+
| |
v v
Product Metadata Product Images
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v v
Metadata Cleaning Image Validation
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v v
One-Hot Encoding RGB + Resize
| |
| v
| Color Histogram
| |
+---------+---------+
|
v
Feature Normalization
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v
Feature Fusion
|
v
Cosine Similarity
|
v
Top-K Recommendations …