Clustered Web Navigation Guidance for Lost Users
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Solution Overview
Problem
The non-standard organization of web pages across different websites and the inefficiency of enumerating all possible navigation paths for users due to individual preferences and varying website structures lead to user confusion and resource wastage in web navigation.
Innovation Solution
A central database system employs machine learning and clustering to predict the next web page a user is likely to visit based on their browsing history and characteristics, providing guidance through hyperlinks or interface modifications when users get lost, and uses this data to optimize website design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system tracks and analyzes comprehensive user navigation data across multiple websites, then the accuracy of predicting next web pages improves, but the processing time and computational resources required increase
Solution Approach 1:
The system segments the comprehensive navigation data into user-specific navigation histories and characteristics. Instead of processing all website data uniformly, it divides the data into individual user profiles containing their unique navigation patterns, allowing personalized predictions without reprocessing entire datasets for each prediction.
Solution Approach 2:
The system performs preliminary data collection and storage of user navigation histories and characteristics in advance. By pre-tracking and storing this information in a database, the system avoids the need to reprocess comprehensive navigation data during each prediction task, thus reducing real-time processing time while maintaining high prediction accuracy.
2Productivity
If the system provides personalized navigation guidance for each user, then user navigation efficiency improves, but the complexity of the navigation system increases
Solution Approach 1:
The system enables personalized navigation guidance by utilizing each user's own navigation history and characteristics stored in their profile. The machine learning model automatically generates personalized recommendations based on the user's unique patterns without requiring manual configuration or complex user-specific programming, thus achieving personalization without proportionally increasing system complexity.
Solution Approach 2:
The system employs a universal machine learning model that can handle multiple users with different navigation patterns through a single unified architecture. This multi-functional model processes diverse user data (navigation histories, characteristics, website structures) using the same underlying algorithms, achieving personalized guidance for all users without requiring separate specialized systems for each user type.
3Quantity of substance
If the system enumerates all possible navigation paths for comprehensive analysis, then the completeness of navigation options improves, but the processing intensity and efficiency deteriorate
Solution Approach 1:
Instead of uniformly analyzing all possible navigation paths across the entire website, the system applies local quality by focusing analysis on paths relevant to each individual user based on their specific navigation history and characteristics. The machine learning model identifies and processes only the locally relevant navigation patterns for each user, reducing processing intensity while maintaining comprehensive coverage for personalized recommendations.
Data Source
AI summary
A central database system provides predictive web navigation using machine learning and clustering to guide a user to a web page. After tracking a number of web pages viewed by various users on one or more web domains and the orders in which these web pages are viewed, the central database system can train a model to predict which web page a user is likely to view next. If the user is lost while navigating, the central database system may guide the user to the predicted web page. In one example of guidance, the central database system presents a web element with a hyperlink to the predicted web page. For example, the central database system can modify a web page that the lost user is presently viewing to include an iframe with a recommendation to travel to a different web page.