URL Content Domain Prefetching for Browser Insight
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Solution Overview
Problem
Web browsers often display unfamiliar or inappropriate content when users input incorrect URLs or click on links, lacking insight into the content domain before user interaction, which can lead to irrelevant or unwanted content being accessed.
Innovation Solution
A system that parses web pages to identify URLs, prefetches content, determines the content domain using machine learning and natural language processing, and compares it to the user's profile based on browsing history, presenting notifications or visual cues if the content is unfamiliar, allowing users to assess potential interest before clicking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the web browser prefetches content from URLs before user interaction, then the user can be provided with content domain insight, but additional processing steps and system complexity are introduced
Solution Approach 1:
The system prefetches content from URLs before the user actually interacts with them by clicking or navigating. This preliminary action allows the content to be analyzed and categorized in advance, so that when the user does interact with the URL, the content domain insight is already available. The prefetching occurs in the background without requiring additional user actions, thus providing information insight while maintaining a relatively simple user interface.
2Measurement precision
If the system analyzes and categorizes prefetched content to determine content domain, then user profile alignment can be assessed, but processing time and energy consumption increase
Solution Approach 1:
The system extracts only the essential content domain characteristics from the prefetched content rather than analyzing every detail. By focusing on extracting key features that define the content domain (such as main topics, categories, or thematic elements), the system achieves sufficient measurement precision for profile alignment assessment while significantly reducing the processing energy required compared to comprehensive content analysis.
3Loss of information
If the system presents notifications for URLs with unfamiliar content, then users can make informed decisions, but the user interface becomes more complex with additional notifications
Solution Approach 1:
The system introduces a content domain categorization system as an intermediary layer between the URL and the user. Instead of presenting raw content or complex analysis results, the intermediary categorizes content into domain labels that are displayed alongside or near the URL. This intermediary representation provides users with content awareness in a simplified, easily digestible format that integrates smoothly into the existing interface without requiring major structural changes or overwhelming the user with notifications.
Data Source
AI summary
In an approach for providing reading insight and notification on a URL with unfamiliar content for a user, a processor parses a web page to identify a URL. The URL references a subsequent web page. Prior to receiving a user interaction with the URL, a processor prefetches content of the subsequent web page. A processor determines a content domain of the prefetched content, the content domain being a summary of the prefetched content. A processor compares the content domain to a user profile, wherein the use profile is based, at least in part, on a browsing history of a user. A processor determines that the content domain is not in alignment to the user profile. A processor presents a notification to the user.