Mindplex Hyperon is a robust, transparent, and explainable recommendation engine that leverages AtomSpace, a metagraph-based knowledge graph developed by the OpenCog project. AtomSpace provides a flexible, semantic framework for representing and querying relationships between concepts, supporting advanced reasoning and AI-driven applications.
The core aim of Mindplex Hyperon is to build recommendations that are not only accurate but also interpretable and trustworthy. By utilizing AtomSpace, the engine can mimic user behavior from historical data, constructing an agent that predicts how a user will interact with future content or features. This agent-based modeling enables the system to provide recommendations with clear, logical explanations, helping users understand the reasoning behind each suggestion.
Key features include:
- Transparent and explainable recommendations
- User-centric agent modeling based on interaction history
- Integration with AtomSpace for semantic knowledge representation
- Support for logic-based graph processing and advanced analytics
Mindplex Hyperon sets a new standard for recommendation systems by focusing on transparency, explainability, and robust knowledge graph integration. The project is designed for extensibility, allowing researchers and developers to build upon its foundation for a wide range of AI and data-driven applications.
This branch is dedicated to the experimental implementation of neural-based recommendation systems. The focus is on exploring, prototyping, and evaluating state-of-the-art neural network architectures and techniques within the Mindplex Hyperon framework.
Guidelines for contributors:
- Select a relevant neural recommendation model or research paper for implementation.
- Document the chosen model or paper, your implementation approach, and analysis in the
docsfolder. Include a summary, key algorithms or architectures, and details on integration with Mindplex Hyperon. - Create a dedicated folder with the paper's name for your implementation in the root directory.
- Ensure your code is modular and adheres to the project's contribution standards.
- Provide clear explanations and comments to support understanding and future development.
This branch is intended as a collaborative environment for advancing neural recommendation research and experimentation, supporting the development of practical, explainable AI systems.
Each feature has associated test cases located in the features/tests directory. The test files are named with a -test suffix to facilitate CI/CD recognition.
- Feature One Tests: Located in
features/tests/FeatureOne-test.metta - Feature Two Tests: Located in
features/tests/FeatureTwo-test.metta
This project utilizes GitHub Actions for continuous integration and deployment. The CI/CD workflow is configured to run all test files upon every pull request to ensure that new changes do not break existing functionality.
We welcome contributions to Mindplex Hyperon! Please refer to the CONTRIBUTING.md file for guidelines on how to contribute, including the requirement for tests in every pull request and naming conventions.
To set up the project locally, clone the repository and install any necessary dependencies. Follow the instructions in the CONTRIBUTING.md file for detailed setup and contribution guidelines.
Refer to the individual feature files for usage examples and implementation details. Each feature is designed to be modular and can be integrated into larger systems as needed.