Personalized Customer Recommendation System

This repository contains a highly advanced, production-ready personalized recommendation system for an e-commerce platform. The system leverages collaborative filtering, content-based filtering, and deep learning techniques, and is designed to scale with cloud infrastructure using AWS for real-time recommendations.

Features

• Collaborative Filtering & Content-Based Filtering: Combines user-product interactions and metadata for personalized recommendations.
• Advanced Data Preprocessing: Automatically handles missing values, normalization, and feature extraction from raw data.
• Hyperparameter Tuning: Utilizes grid search to find the optimal parameters for the deep learning model.
• AWS S3 Integration: Stores and retrieves datasets from AWS S3 for large-scale data processing.
• Dockerized Application: Runs in a containerized environment using Docker for easy deployment.
• Real-time Recommendation System: Processes new user data in real-time for instant recommendations.

System Architecture

1. Data Generation: Synthetic data generation to simulate a large-scale e-commerce platform.
2. Preprocessing: Data cleaning and transformation for collaborative filtering.
3. Model Training: Deep learning models for personalized recommendations using TensorFlow and Keras.
4. Evaluation: Model evaluation and tuning using hyperparameter optimization and A/B testing.
5. Real-time Processing: Integrated with AWS S3 for cloud storage and real-time recommendation processing.

Project Structure

• config/: Configuration files for model parameters and project settings.
• data/: Scripts to generate synthetic data and preprocess it for training.
• dataset/: Raw and processed datasets.
• models/: Model training and evaluation scripts.
• notebooks/: Jupyter notebooks for exploratory data analysis.
• aws/: AWS integration scripts for dataset uploads and processing.
• Dockerfile: Docker configuration for the project environment.
• requirements.txt: Python dependencies.

Running the Project

1. Clone the repository:

   git clone https://github.com/your-username/personalized_customer_recommendation_system.git
   cd personalized_customer_recommendation_system