Search Result Filters Learned From Responsive Resource Content
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Solution Overview
Problem
Existing search systems require users to manually generate filters or rely on hardcoded filters that demand expert knowledge, and they struggle to adapt to the dynamic nature of online content, often leading to incomplete or ineffective search results.
Innovation Solution
A system that automatically learns search query filters from the content of responsive resources, extracting keywords and applying diversity criteria to provide tailored filters that adapt to user needs and available results in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users manually generate filters or rely on hardcoded filters, then filter functionality can be provided, but user effort and system complexity increase while adaptability to dynamic content decreases
Solution Approach 1:
The system automatically generates filters by analyzing search results and identifying relevant filtering criteria without requiring manual user input or expert knowledge. The filter generation process serves itself by extracting keywords and concepts directly from the search results, enabling dynamic adaptation to changing content while reducing user effort and system complexity
Solution Approach 2:
The system dynamically adjusts filter parameters based on the characteristics of search results. By analyzing keywords, content types, and result distributions, the system automatically modifies filter criteria to match the current search context, enabling adaptability to dynamic content without hardcoding specific filter configurations
2Ease of operation
If hardcoded filters are used, then filter functionality is available, but expert knowledge and programming are required ahead of time
Solution Approach 1:
The system automatically generates appropriate filters by analyzing search results and identifying relevant criteria, eliminating the need for users to have expert knowledge or programming skills. The self-service approach extracts filtering opportunities directly from the search context and presents ready-to-use filters to users
Solution Approach 2:
The system performs preliminary analysis of search results to identify and prepare filter options before user interaction. By pre-processing search results to extract keywords and determine relevant filtering dimensions, the system makes filter generation seamless and user-friendly without requiring advance configuration or expert input
3Productivity
If traditional search systems are used, then basic search functionality is provided, but filtering capability within search results is limited
Solution Approach 1:
The system segments search results into distinct categories and dimensions that can be independently filtered. By analyzing search results to identify natural groupings such as content types, sources, or thematic categories, the system creates multiple filter dimensions that enable users to efficiently narrow down results while adapting to the specific search context
Solution Approach 2:
The system dynamically generates filter options based on the characteristics of current search results rather than using static, pre-defined filters. This dynamic approach allows the filtering capability to adapt to different search queries and result sets, improving both search efficiency and versatility by providing context-relevant filter options
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing filters from resource content. In one aspect, a system receives data identifying a set of resources that are determined to be responsive to a search query and extracts a set of keywords from the contents of the resources and related queries. The keywords are processed according to candidate selection criteria, and a set of candidate query filters are determined. The candidate filters may be used to filter the resources that are responsive to the query.