Search Query Prefix Processing with Cumulative Resource Weighting
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Solution Overview
Problem
Existing search engine systems face challenges in improving accuracy, processing speed, and memory usage when identifying display data associated with suggested search queries during user input, as they often rely on outdated methods that do not effectively prioritize resource weights based on user behavior.
Innovation Solution
The system calculates a cumulative resource weight based on the likelihood of user access to resources associated with suggested search queries, allowing for more accurate and faster processing by prioritizing display data from resources with higher weights, which are determined before user input is received, and transmitting this data alongside suggested search queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the system processes display data identification in real-time during user input, then processing speed is improved, but memory usage increases
Solution Approach 1:
The system pre-calculates and stores cumulative resource weights for multiple suggested search queries before the user completes their input. This preliminary action allows the system to quickly retrieve pre-computed weights during real-time processing, achieving fast response times without requiring excessive memory during the critical user interaction phase.
Solution Approach 2:
The system focuses computational resources on calculating cumulative weights only for the top N suggested search queries that are most likely to be selected by the user. By applying quality differentiation to local elements (top suggestions vs. other suggestions), the system achieves accurate real-time processing while minimizing overall memory consumption.
2Measurement precision
If the system uses cumulative resource weight based on user access likelihood, then accuracy is improved, but device complexity increases
Solution Approach 1:
The system introduces cumulative resource weight as an intermediary metric that bridges user behavior patterns and search query selection. This intermediary allows the system to accurately predict user intent without requiring complex direct analysis of user behavior, simplifying the overall system architecture while maintaining high accuracy.
Solution Approach 2:
The system transforms complex user behavior analysis into a simplified parameter (cumulative resource weight) that can be easily calculated and compared. By changing the parameter representation from complex behavioral patterns to a single numerical weight, the system achieves high accuracy in predicting user selection while reducing computational complexity.
3Speed
If the system calculates resource weight before user input is received, then processing speed is improved, but memory usage increases
Solution Approach 1:
The system performs preliminary calculation of cumulative resource weights for suggested search queries and stores them in advance. This pre-computation allows the system to quickly retrieve and use these weights when processing user input, achieving fast response times without requiring intensive real-time calculations that would consume excessive memory during user interaction.
Solution Approach 2:
The system calculates cumulative weights for all suggested search queries in advance, which is more than strictly necessary. However, this excessive preliminary action ensures that during real-time user input processing, the system can immediately retrieve the needed weights without any calculation delay, optimizing speed while the memory cost is incurred during off-peak times.
Data Source
AI summary
A system for and a method of processing a prefix associated with a search query. The method comprises receiving a user input defining a prefix and identifying a list of suggested search queries associated with the prefix. The method determines that (i) a first suggested search query is associated with a first resource and (ii) a first cumulative resource weight based on a first relation between the first suggested search query and the first resource is no less than a second cumulative resource weight based on a second relation between a second suggested search query and a second resource. If so, the method then identifies display data associated with a content item associated with the first resource and transmits, prior to a completed search query associated with the prefix being submitted to the search engine, to the electronic device, the display data along with the list of suggested search queries.