Query Association Engine for Multi-Source Search Refinement
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Solution Overview
Problem
Search engines lack the ability to automatically generate modified queries for multiple content sources based on previous user modifications, leading to suboptimal search results across different content sources, and there is no mechanism for users to leverage query modifications made by others or store and retrieve personalized query modifications.
Innovation Solution
The system creates query associations between initial queries and their modifications on a per-content source basis, allowing for automatic generation of modified queries using user-specific and community-created modifications, with ranking schemes to determine relevance, and enabling users to store and apply query templates for future searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single query is used to search across multiple content sources, then the search process is simple and fast, but the search results are not optimal for each specific content source
Solution Approach 1:
The system segments the search process by creating separate query modifications for each content source. Instead of using a single generic query across all sources, the system divides the query into source-specific versions, allowing each content source to receive a tailored query that optimizes search results for that particular source type.
Solution Approach 2:
The system performs preliminary actions by pre-defining query modifications for different content sources. These modifications are prepared in advance and stored in an database, so when a user submits a query, the system can automatically apply the appropriate pre-prepared modifications rather than requiring manual query rewriting for each source.
2Manufacturing precision
If users manually modify queries for each content source, then search result relevance improves, but user time and effort increase
Solution Approach 1:
The system enables self-service by automatically generating and applying source-specific query modifications without requiring manual user intervention. The system uses predefined modification templates and automatically selects and applies the appropriate modifications based on the target content source, allowing the search system to serve itself rather than requiring continuous user input.
Solution Approach 2:
The system implements feedback mechanisms where user interactions with search results are analyzed to refine and update query modifications. The system learns from user behavior patterns and continuously improves the query modifications, creating a feedback loop that enhances search relevance over time without requiring explicit user input for each search.
3Stability of the object's composition
If query modifications are stored for future use, then search consistency improves, but system complexity increases
Solution Approach 1:
The system achieves universality by creating a multi-functional query modification database that serves multiple purposes: storing predefined modifications, automatically selecting appropriate modifications based on content source, applying modifications to queries, and providing suggestions to users. This single database structure handles multiple functions that would otherwise require separate systems.
Data Source
AI summary
Techniques are provided through which query associations are made between initial queries and corresponding query modifications to the initial queries previously made. Each query modification is made with respect to a searchable source. Based on these query associations, modified queries are generated on a per-source basis. Query associations may be stored on a per user or per community basis, where a community can include many users. Frequently used query modifications may be stored as templates.