Overview

This project develops a machine learning based recommendation system for fashion e-commerce, helping users find products that match their preferences in terms of size, color, and style. By leveraging Content Based Filtering and Collaborative Filtering, the system analyzes product attributes and user interactions to generate personalized recommendations, reducing search time and improving the shopping experience.

The Content Based Filtering approach utilizes cosine similarity to identify similar products based on categorical attributes like brand, category, and color. Meanwhile, Neural Collaborative Filtering (NCF) enhances recommendations by predicting user preferences based on past interactions and ratings. This solution not only increases customer satisfaction but also optimizes sales for e-commerce platforms.

Found out more on my Github