This Azure Function is designed to provide article recommendations using a collaborative filtering approach. It utilizes the Implicit library and a pre-trained model to suggest articles based on a user's preferences.
Before deploying and using this Azure Function, make sure you have the following prerequisites:
- Azure account: You need an active Azure account to deploy and host this Azure Function.
- Implicit library: The function uses the Implicit library for collaborative filtering. Make sure you install this library or ensure it's included in your deployment package.
- Pre-trained model: The function relies on a pre-trained model for generating recommendations. Ensure this model is available in the same directory as the function.
- CSR data: The function loads CSR data from the csr_article_popularity.npz file. Make sure this file is available in the same directory.
- Python environment: The function is written in Python. You should have a Python environment (3.6 or higher) for running this function.
The Azure Function accepts a single parameter, "user_id," either through a query string or a JSON request body. It uses the user_id to generate personalized article recommendations.
The function returns a JSON response containing a list of recommended article IDs. The recommendations are based on the user's preferences and the collaborative filtering model.
To get recommendations for a user, you can send an HTTP request to the Azure Function with the user_id as a parameter.
Example using cURL:
curl -X GET "https://your-function-url.azurewebsites.net/api/HttpTrigger?user_id=123"The response will be a JSON array of recommended article IDs:
["12345", "67890", "54321", "98765", "11223"]You can deploy this Azure Function to your Azure account using Azure Functions. Make sure to set up the necessary environment and configurations, including your Azure credentials.
This Azure Function is available under the MIT License.