guess-js/guess
Guess
Guess is a predictive page loading library that uses machine learning to prefetch JavaScript bundles and assets. It functions as a resource prefetcher that predicts the next page a user will visit by utilizing a web application route parser and a user behavior analytics integrator.
The project distinguishes itself by importing navigation patterns from analytics APIs to inform its predictive models. It uses probabilistic navigation modeling and historical transition data to calculate the likelihood of future page visits, allowing for the proactive download of lazy-loaded bundles.
The system includes capabilities for static-analysis route extraction to map application paths to specific code bundles. It also features network-adaptive prefetching, which adjusts the intensity of resource loading based on network quality and user data preferences.
Features
- Page Navigation Prefetching - Predicts likely next page visits using machine learning to proactively fetch assets and reduce load times.
- Predictive Loading Libraries - A capability to identify and download lazy-loaded JavaScript bundles for the pages most likely to be visited next.
- User Navigation Prediction - Implements a logic engine that determines the most likely next page a user will visit to trigger proactive resource fetching.
- User Navigation Analytics - Calculates the probability of a user visiting specific pages based on current paths and historical navigation data.
- Probabilistic Models - Calculates the likelihood of future page visits by analyzing historical user transition data.
- Model Population Routines - Imports structured transition reports from external APIs to train and update navigation predictions.
- Behavioral Analytics - Imports navigation patterns from analytics APIs to inform predictive resource fetching.
- Historical Data Import - Extracts historical transition reports from analytics providers to drive navigation predictions.
- Navigation Pattern Retrieval - Retrieves structured user behavior and transition data from analytics APIs to inform predictive loading.
- Route-Based Static Analysis - Scans source code to build a map of all application routes and their associated assets.
- Automated Route Mapping - Scans source code and framework files to identify application paths and link them to specific resource bundles.
- Predictive Fetching - A system that analyzes navigation patterns to identify likely next pages and fetch resources to reduce perceived load times.
- Framework Route Parsing - Extracts route information from framework files to determine application structure for predictive loading.
- ML-Driven Prefetchers - Predicts the next page a user will visit to prefetch JavaScript bundles and assets using machine learning.
- Route Extraction Tools - Implements static analysis to scan project source code and identify all active web application routes.
- Route Extraction Utilities - Provides a utility that scans project source code to map application routes to their corresponding JavaScript bundles.
- Route Map Generation - Generates data maps of registered routes and parameterizes them to analyze user navigation patterns.
- Route-to-Bundle Mappings - Links application paths to specific code bundles via automated parsing to optimize asset loading.
- Route Parameterization - Aggregates specific page URLs into generic parameterized patterns to simplify navigation behavior analysis.
- Web Performance Optimization Tools - Improves application speed by automatically mapping routes to bundles and proactively downloading necessary code.
- Network-Aware Content Loading - Adjusts the intensity of resource prefetching based on current network quality and user data constraints.
- Adaptive User Experiences - Integrates analytics and navigation patterns to deliver content based on predicted user behavior.
- Adaptive Resource Prefetching - Adjusts the prefetching of web resources based on network quality and user data preferences to save bandwidth.
- Network-Adaptive Asset Loading - Adjusts the intensity of resource prefetching based on current network quality and user data constraints.
- Resource Loading - Predictive prefetching using analytics and machine learning.
