Predictive Preloading with Machine Learning

Outline a basic implementation of using machine learning models to predict and preload resources that a user is likely to request next, based on their browsing patterns and history.

async function loadModel() {
  // Load the model from the server
  const model = await tf.loadLayersModel('/path/to/your/model.json');
  return model;
}

This function loads the machine learning model using TensorFlow.js. The model is supposed to predict user actions to preload resources effectively.

function predictNextResource(userData) {
  const model = await loadModel();
  // Assuming userData is pre-processed and ready for the model
  const prediction = model.predict(userData);
  return prediction;
}

This function uses the loaded model to predict the next resource the user is likely to request, based on their browsing history or other relevant data.

function preloadResource(resource) {
  const link = document.createElement('link');
  link.rel = 'prefetch';
  link.href = resource;
  document.head.appendChild(link);
}

This function creates preload links for the predicted resources, adding them to the page head to start preloading.

async function predictivePreloading(userData) {
  const nextResource = await predictNextResource(userData);
  // Convert prediction to actual resource URL or path
  const resourceURL = convertPredictionToURL(nextResource);
  preloadResource(resourceURL);
}

This function connects everything: it predicts the next resource using user data, then it preloads the resource by dynamically inserting a prefetch link to the document head.