Search Query Reformulation via Result Term Occurrence Count
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Solution Overview
Problem
Users often need to manually reformulate search queries multiple times to find desired web resources due to the inability of current search engines to accurately disambiguate results without extensive user input or historical data, leading to increased time and complexity in the search process.
Innovation Solution
A system that automatically reformulates search queries by displaying rank-ordered terms on the search results page, allowing users to promote, demote, or remove terms, which are derived from the frequency of occurrence in search results, eliminating the need for manual iterative reformulation and advanced search syntax.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search engines rely on the perfect search engine hypothesis to perfectly derive user intent, then search accuracy improves, but the complexity of query disambiguation and user information requirements increase significantly
Solution Approach 1:
The system automatically analyzes search result data to identify and rank disambiguation terms without requiring users to manually input extensive information or navigate complex advanced search modes. The search engine itself performs the disambiguation work by examining term frequency and distribution in results, making the system self-servicing rather than relying on perfect user input
Solution Approach 2:
The system changes the parameter of query disambiguation from requiring extensive user-provided contextual information to using automatically derived term frequency statistics from search results. By shifting from user-centric information input to system-centric data analysis, the complexity burden moves from the user side to the system side while maintaining or improving accuracy
2Measurement precision
If users manually reformulate queries through advanced search modes, then query precision improves, but the time and operational complexity increase
Solution Approach 1:
The system performs preliminary analysis of search results to pre-identify relevant disambiguation terms and their rankings before the user needs to reformulate the query. By having terms ready and ranked in advance based on their frequency and distribution in the results, the user can quickly select terms without manual analysis or iterative reformulation attempts
Solution Approach 2:
The system extracts key disambiguation terms automatically from the search result data itself, separating the term identification task from the user's manual reformulation process. By extracting and presenting only the most relevant terms with their frequency information, the system reduces the reformulation task from examining all possible terms to selecting from a curated, ranked list
3Measurement precision
If users provide extensive personal information and search history for query disambiguation, then search relevance improves, but user privacy requirements and system data collection complexity increase
Solution Approach 1:
The system uses the search result data itself as the information source for disambiguation, making the results serve dual purposes: both answering the query and providing the basis for term selection. This self-service approach eliminates the need to separately collect user personal information or search history, as the result data contains the necessary signals for disambiguation
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing voice commands. In one aspect, a method includes obtaining an occurrence count for terms that occur in resources that a search engine has identified as being responsive to an original search query, identifying a term that occurs in the resources, based on the occurrence count, providing the term and a control for display on a client device, the control being associated with the term and with promotion or demotion criteria, receiving a signal indicating that the user has selected the control, and automatically reformulating the original search query based on the term and the promotion or demotion criteria.