Engines Module Documentation

The engines module is a comprehensive framework designed to implement various machine learning algorithms and techniques for recommendation systems. It includes several submodules, each focusing on different aspects of recommendation and filtering.

Submodules

• contentFilterEngine: A versatile engine for content-based filtering, incorporating traditional machine learning algorithms, neural network models, and hybrid approaches.
• unionizedFilterEngine: Focuses on collaborative filtering techniques, including matrix factorization and neural network-based methods.
• hybrid: Combines multiple recommendation strategies to enhance accuracy and robustness.
• content_based: Implements content-based filtering techniques, leveraging item features for recommendations.

Usage

The engines module is designed to be modular and extensible, allowing developers to easily integrate and experiment with different algorithms and techniques. Each submodule can be imported and utilized independently, providing flexibility in building custom recommendation solutions.

Example

from engines.contentFilterEngine.traditional_ml_algorithms import TRA_LR
model = TRA_LR()
model.fit(X_train, y_train)
predictions = model.predict(X_test)

Notes

• Ensure that all dependencies are installed and configured correctly before using the module.
• The module is intended for educational and research purposes and may require further optimization for production use.

Project Layout

engines/
    contentFilterEngine/
    unionizedFilterEngine/
    hybrid/
    content_based/
docs/
    index.md  # The documentation homepage.
    ...       # Other markdown pages, images, and other files.

For more detailed documentation on each submodule, refer to the respective markdown files in the docs directory.