Search Prediction Engine Using Context Modeling
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Solution Overview
Problem
Current search engines lack the ability to effectively predict subsequent search actions based on patterns of user searches, which hampers the enhancement of search results and user experience.
Innovation Solution
A search prediction engine that tracks user search queries and compares them with histories from other users, assigning greater computation weight to similarities, allowing for accurate prediction of future queries by contextual matching and caching results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search engines track and compare user search query histories to generate predictions, then search result relevance and user experience are improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the search system into distinct functional modules: a tracking component that collects search queries, a comparison engine that analyzes query histories, and a prediction module that generates suggestions. This segmentation allows each component to specialize in specific tasks, improving overall system efficiency and relevance prediction accuracy while managing complexity through modular design
Solution Approach 2:
The system performs preliminary actions by pre-tracking and storing user search query histories before actual search operations occur. This advance preparation enables the comparison engine to quickly retrieve and analyze historical data during real-time searches, improving response speed and prediction accuracy without adding complexity to the core search functionality
2Measurement precision
If search engines compare user search histories with other users to generate predictions, then prediction accuracy improves, but computational time and processing power increase
Solution Approach 1:
The patent applies local quality by weighting comparisons based on user similarity - not all user histories are treated equally. The system identifies users with similar search patterns and gives greater weight to their historical data when generating predictions. This selective approach improves prediction accuracy for individual users while reducing unnecessary computational effort compared to analyzing all user histories uniformly
Solution Approach 2:
The system dynamically adjusts computational parameters based on query patterns and user profiles. By changing parameters such as the number of comparable users to analyze, the depth of historical data reviewed, and the weighting factors applied to different users' histories, the system optimizes the balance between prediction accuracy and computational time for different search scenarios
Data Source
AI summary
A search engine tracks search queries from a user received at the search engine to generate a search query history. A prediction engine compares the search query history from the user with search query histories from other users on the search engine. A predicted search query for the user that the user is predicted to use to perform a next search in relation to other possible searches is generated in view of the comparison. Search query histories of the other users with greater similarities to the search query history of the user have a greater computation weight in generating the predicted search query than the search query histories of the other users with fewer similarities to the search query history of the user.