H&M Fashion Recommendation System

Project Overview

This project develops a fashion recommendation system for H&M products using a hybrid approach combining collaborative filtering and content-based methods. The system uses historical purchase data, product descriptions, and customer attributes to generate personalized fashion recommendations.

Features Implemented

• Hybrid Recommendation Engine - Combines collaborative filtering and content-based approaches
• Product Embeddings - Generated from product descriptions and attributes
• User Preference Modeling - Learns customer preferences from historical purchases
• Seasonal Trend Analysis - Incorporates temporal patterns in fashion preferences
• Evaluation Framework - Comprehensive metrics for recommendation quality assessment

Implementation Details

The recommendation system includes several key components:

1. Data Preprocessing - Cleaning and transformation of H&M product and transaction data
2. Feature Engineering - Extraction of relevant features from text descriptions and metadata
3. Collaborative Filtering Model - Matrix factorization approach for user-item interactions
4. Content-Based Model - Leverages product attributes and descriptions
5. Hybrid Model - Combines multiple recommendation approaches for optimal results
6. Evaluation Pipeline - Metrics include precision, recall, and ranking-based measures

Learning Outcomes

This project allowed me to:

• Develop expertise in building hybrid recommendation systems
• Implement techniques for handling sparse user-item interaction data
• Apply NLP techniques to extract meaningful features from product descriptions
• Design effective evaluation strategies for recommendation systems

GitHub Repository

The complete source code is available on my GitHub repository: github.com/apratim-mishra/h_m