Context-Relevant Website Prediction Engine
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems fail to effectively predict and provide users with context-relevant information without requiring explicit search queries, as they lack the ability to analyze user context and aggregate user activity data to identify frequently visited websites based on location, interests, and other attributes.
Innovation Solution
A system that receives user context data, including location, interests, and activities, to determine likely websites visited by users in similar contexts, using a prediction engine to generate session queries and compute relevance signals, and provides links to these websites without a search query, filtering out less relevant ones based on geographic and interest-specific criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the system provides context-relevant website predictions without requiring explicit search queries, then information accessibility and user convenience are improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing user activity data, context information, and website visit patterns in advance. This allows the prediction engine to quickly retrieve and analyze pre-organized data when generating context-relevant website predictions, reducing real-time processing complexity while maintaining high user convenience
Solution Approach 2:
The patent introduces a prediction engine as an intermediary component that sits between raw user data and the final website recommendations. This intermediary layer aggregates user context, analyzes activity patterns, and generates predictions, thereby managing system complexity through modular architecture while providing seamless user experience
2Measurement precision
If the system analyzes large-scale user activity data to predict interesting content, then prediction accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent segments user activity data into distinct contexts (location, time, device type, user attributes) and processes each segment separately. The prediction engine analyzes specific context segments independently and combines results, which improves prediction accuracy through focused analysis while reducing overall processing time through parallelization of segmented data streams
Solution Approach 2:
The system applies partial action by selecting and analyzing only the most relevant user activity segments and context factors needed for specific prediction scenarios, rather than processing all available data uniformly. This selective approach maintains high prediction accuracy for targeted queries while significantly reducing computational overhead and processing time
3Loss of information
If the system provides personalized website recommendations based on user context, then information relevance is improved, but the complexity of data aggregation and analysis increases
Solution Approach 1:
The prediction engine is designed with multi-functionality to handle diverse user contexts (geographic location, time of day, device type, user attributes) and generate appropriate website predictions for each scenario. This universal approach consolidates multiple analysis functions into a single system, improving information relevance across different user situations while managing aggregation complexity through unified processing logic
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting context-relevant information based on large-scale aggregations of data. One of the methods includes receiving a user context of a user, wherein the user context specifies a location of a user device being used by the user. Data that represents counts of websites visited by users matching the user context is obtained. Data that represents counts of websites visited by users in general is obtained. Using the obtained counts, one or more likely websites visited by users matching the user context more frequently than by users in general is determined. Information identifying the one or more likely websites in response to receiving the user context is provided.