Recommendation Systems

Project Overview

This project aims to create a recommendation engine for an e-commerce platform to suggest products to users based on their browsing and purchase history. The recommendation system will utilize various machine learning techniques, including collaborative filtering, content-based filtering, matrix factorization, and neural networks, to provide personalized product recommendations.

Components

1. Data Collection and Preprocessing

Collect and preprocess data related to user interactions on the e-commerce platform, including browsing history, purchase history, product details, and user information. Ensure the data is clean, consistent, and ready for analysis.

• Data Sources: User interactions (browsing and purchase history), product information (category, price, etc.), and user profiles (demographics, preferences).
• Techniques Used: Data cleaning, normalization, handling missing values, feature engineering.

2. Collaborative Filtering

Implement collaborative filtering techniques to recommend products based on the preferences of similar users.

• Techniques Used: User-based collaborative filtering, item-based collaborative filtering, k-nearest neighbors.

3. Content-Based Filtering

Develop content-based filtering models to recommend products similar to those the user has interacted with in the past.

• Techniques Used: TF-IDF, cosine similarity, feature extraction.

4. Matrix Factorization

Use matrix factorization techniques to decompose the user-item interaction matrix and identify latent features for recommendations.

• Techniques Used: Singular Value Decomposition (SVD), Alternating Least Squares (ALS).

5. Neural Networks

Leverage neural networks to create advanced recommendation models that can capture complex patterns in the data.

• Techniques Used: Deep learning, embeddings, autoencoders, neural collaborative filtering.

Project Structure

recommendation_systems/ ├── data/ │ ├── raw/ │ ├── processed/ ├── notebooks/ │ ├── data_preprocessing.ipynb │ ├── collaborative_filtering.ipynb │ ├── content_based_filtering.ipynb │ ├── matrix_factorization.ipynb │ ├── neural_networks.ipynb ├── models/ │ ├── collaborative_filtering_model.pkl │ ├── content_based_filtering_model.pkl │ ├── matrix_factorization_model.pkl │ ├── neural_network_model.h5 ├── src/ │ ├── data_preprocessing.py │ ├── collaborative_filtering.py │ ├── content_based_filtering.py │ ├── matrix_factorization.py │ ├── neural_networks.py ├── README.md ├── requirements.txt ├── setup.py

Getting Started

Prerequisites

• Python 3.8 or above
• Required libraries listed in requirements.txt

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/recommendation_systems.git
   cd recommendation_systems
   

2. Install the required packages:

    pip install -r requirements.txt
   

Data Preparation

1. Place raw data files in the data/raw/ directory.
2. Run the data preprocessing script to prepare the data:

   python src/data_preprocessing.py
   

Running the Notebooks

1. Launch Jupyter Notebook:

   jupyter notebook
   

2. Open and run the notebooks in the notebooks/ directory to process data, build models, and evaluate results:

• data_preprocessing.ipynb
• collaborative_filtering.ipynb
• content_based_filtering.ipynb
• matrix_factorization.ipynb
• neural_networks.ipynb

Training Models

1. Train the collaborative filtering model:

   python src/collaborative_filtering.py
   

2. Train the content-based filtering model:

   python src/content_based_filtering.py
   

3. Train the matrix factorization model:

   python src/matrix_factorization.py
   

4. Train the neural network model:

   python src/neural_networks.py
   

Results and Evaluation

• Collaborative Filtering: Evaluate the performance using metrics such as Precision@K, Recall@K, and Mean Average Precision (MAP).
• Content-Based Filtering: Measure the model's accuracy using metrics like Precision, Recall, and F1-score.
• Matrix Factorization: Assess the model with Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE).
• Neural Networks: Evaluate the deep learning models using metrics such as Precision, Recall, F1-score, and AUC-ROC.

Contributing

We welcome contributions from the community. Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Create a new Pull Request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

• Thanks to all contributors and supporters of this project.
• Special thanks to the data scientists and engineers who provided insights and data.