Navigation Prediction System for Web Content Prerendering
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Solution Overview
Problem
Current high-speed Internet services still result in delays when users navigate web pages, leading to lost productivity due to the time spent waiting for content to load.
Innovation Solution
A method and system that predicts user navigation events by analyzing indicators such as browsing history, text entry, and cursor position, allowing for prerendering of content associated with the predicted next navigation event, thereby reducing wait times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content is prerendered based on predicted navigation events, then user productivity is improved by reducing wait time, but system resources are consumed by preemptive content requests
Solution Approach 1:
The system performs preliminary actions by predicting future navigation events and prerendering content before users actually request it. The navigation prediction module analyzes browsing history, cursor position, and text entry to forecast which pages users will visit next, then triggers prerendering of those predicted pages in advance, so when users navigate to them, the content is already loaded and ready for immediate display.
Solution Approach 2:
The system applies partial action by selectively prerendering only certain predicted navigation events rather than all possible pages. The navigation prediction module calculates confidence values for different predicted navigation events and prioritizes prerendering high-confidence predictions, thus consuming system resources only for the most likely navigation events rather than uniformly prerendering all potential content.
2Measurement precision
If navigation history data is collected and stored, then prediction accuracy is improved, but user privacy is compromised through collection of personal information
Solution Approach 1:
The system extracts and removes personally identifiable information from navigation history data before storage and processing. The anonymization module processes collected navigation events to strip out identifying details such as user names, email addresses, and other personal identifiers, retaining only the navigational patterns and behavioral data needed for accurate prediction while protecting user privacy.
Solution Approach 2:
The system introduces an intermediary anonymization layer between data collection and data processing. The anonymization module acts as a mediator that receives raw navigation history containing personal information, processes it to remove identifying details, and outputs anonymized data suitable for prediction algorithms, thus enabling accurate prediction while preventing direct access to personal information.
Data Source
AI summary
A method and system for predicting a next navigation event are described. Aspects of the disclosure minimize the delay between a navigation event and a network response by predicting the next navigation event. The system and method may then prerender content associated with the next navigation event. For example, the method and system may predict a likely next uniform resource locator during web browsing to preemptively request content from the network before the user selects the corresponding link on a web page. The methods describe a variety of manners of predicting the next navigation event, including examining individual and aggregate historical data, text entry prediction, and cursor input monitoring.