Recommendation Model using Milvus vector db

Title: Recommendation Model Using Milvus Vector Database with Docker

Read About Vector Databases :https://zilliz.com/authors/Samin_Chandeepa

Overview

This repository contains an implementation of a recommendation model using Milvus, a vector database designed to store and search high-dimensional vectors efficiently, deployed with Docker. The recommendation model leverages the capabilities of Milvus to perform fast similarity searches, enabling efficient retrieval of similar items based on user preferences or item attributes.

Features

• Utilizes Milvus for efficient storage and retrieval of high-dimensional vectors.
• Implements recommendation algorithms for personalized recommendations.
• Supports both user-based and item-based recommendation strategies.
• Provides easy-to-use APIs for integrating the recommendation model into existing applications.
• Supports scalability for handling large datasets and user bases.
• Dockerized deployment for easy setup and management.

Installation

1. Clone this repository:

   git clone https://github.com/HGSChandeepa/recommendation-model-milvus.git

2. Navigate to the project directory:

   cd recommendation-model-milvus

3. Build and run the Docker container:

   docker-compose up --build

Usage

1. Prepare your dataset and generate embeddings for items or users using your preferred method.

2. Configure the Milvus instance:

   • Set up a Milvus server and configure connection parameters in config.py.

3. Load the embeddings into Milvus:

   • Use the provided scripts or implement your own to load vectors into Milvus.

4. Initialize the recommendation model:

   • Use the provided classes or implement your own recommendation logic.

5. Interact with the recommendation model:

   • Use the provided APIs to obtain recommendations based on user preferences or item attributes.

Example

Check out the provided Jupyter notebooks (examples/) for a demonstration of how to use the recommendation model with sample datasets.

Contributing

Contributions are welcome! If you have any ideas for improvement or find any issues, please open an issue or submit a pull request.

License

This project is licensed under the MIT License.

Acknowledgements

• Thanks to the creators of Milvus for providing an efficient vector database solution.
• This project was inspired by various recommendation systems and libraries.

Contact

For any questions or inquiries, feel free to contact [email protected].

Happy recommending!