Web Page Change Analysis with Revisitation Patterns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The vast and dynamic nature of internet content makes it difficult for users to access desired information due to the sheer size, scope, and constant alterations of web pages, which can interfere with revisiting previously viewed content.
Innovation Solution
Analyzing change data and revisitation patterns to infer user consumption intent, allowing systems to support interaction by highlighting relevant changes, providing cached information, and optimizing content retrieval based on user interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If web pages are frequently updated and modified to provide current information, then the information currency and relevance are improved, but the difficulty of accessing desired information and user confusion increases
Solution Approach 1:
The system performs preliminary analysis of web page change data and revisitation patterns before user queries occur. By pre-processing and storing characterization data about how web pages change and how users revisit them, the system prepares information in advance to quickly match user needs with appropriate content versions, resolving the contradiction between frequent updates and easy access.
Solution Approach 2:
The system uses revisitation pattern data as feedback to understand user behavior and preferences. By analyzing how users revisit web pages and what changes they notice, the system adjusts its content delivery strategy to highlight relevant changes while maintaining accessibility, thus balancing information currency with ease of operation.
2Measurement precision
If the system monitors and analyzes all web page changes to provide accurate content, then the measurement precision of content changes is improved, but the device complexity and computational resources increase
Solution Approach 1:
The system segments web page monitoring into distinct components: change detection modules that identify modifications, characterization modules that analyze the nature of changes, and revisitation pattern modules that track user behavior. This segmentation allows each component to focus on specific tasks, improving measurement precision while managing system complexity through modular design.
Solution Approach 2:
The system extracts only the essential characteristics from web page changes and revisitation patterns, rather than processing all raw data. By identifying and extracting key features such as change frequency, change type, and revisitation intervals, the system achieves high measurement precision with reduced computational complexity.
3Ease of operation
If the system retrieves and caches all web page content to ensure information availability, then the information accessibility is improved, but the storage requirements and energy consumption increase
Solution Approach 1:
The system applies local quality by caching and retrieving only the specific portions of web pages that are relevant to user needs, rather than storing entire pages. Based on revisitation patterns and change characterizations, the system identifies and caches only the critical content segments, improving information availability while reducing storage and energy requirements.
Solution Approach 2:
The system performs partial action by caching a subset of web page content that is most likely to be revisited or changed, rather than caching all content. This selective caching approach ensures that frequently needed information is readily available while minimizing storage and energy consumption associated with maintaining comprehensive caches.
Data Source
AI summary
Web page change may be related to revisitation patterns to support web interaction. In an example embodiment, a method involves analyzing change and revisitation data for a web page, determining a relationship between the data, inferring consumption intent by a user for the web page, and utilizing the inferred consumption intent. More specifically, change data is analyzed to produce a change characterization, with the change data reflecting differences between content of a web page at different times. Revisitation data is analyzed to produce a revisitation characterization, with the revisitation data including visit times to the web page by a user. A relationship is determined between the change and the revisitation data based on the change and the revisitation characterizations. Consumption intent of the user for the content of the web page is inferred responsive to the relationship. The inferred consumption intent is utilized to support interaction with the web page.