User-Influenced Search Query Language Filtering
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Solution Overview
Problem
Current search engines often provide irrelevant results due to the interpretation of ambiguous search terms and failure to filter out unwanted ingredients, leading to user frustration and potential allergic reactions.
Innovation Solution
The implementation of an enhanced query language system that allows users to specify filters and preferences through syntax like dashes and parentheses, enabling the search engine to generate user-influenced search results by aggregating, filtering, or ordering search results based on user-defined criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a standard text-based search is performed using search terms like 'cookies without nuts', then the search engine retrieves results for each term separately and combines them, but the search results include products with nuts that the user wants to avoid
Solution Approach 1:
The system performs preliminary analysis of the search query to identify exclusion terms (e.g., 'without nuts') and applies filtering rules before final result compilation. This preliminary action ensures that unwanted items are excluded early in the process, improving both accuracy and efficiency.
Solution Approach 2:
The system introduces an intermediary processing layer between the basic text search and the final result presentation. This intermediary layer interprets the user's intent, identifies exclusion criteria, and applies appropriate filtering logic to combine search results correctly, resolving the contradiction between speed and accuracy.
2Device complexity
If the search engine discards search results for the term 'without' as some search engines do, then the search process becomes simpler, but the search results lose important filtering information
Solution Approach 1:
The system extracts the filtering intent from exclusion terms (e.g., 'without') and separates it from the main search query. Instead of discarding these terms, the system isolates them as specific filtering instructions and applies them to the search results, preserving crucial information while maintaining processing efficiency.
Solution Approach 2:
The system changes the interpretation parameter of exclusion terms from 'discard this search term' to 'apply filtering rule'. This parameter change transforms how the search engine processes terms like 'without', converting them from noise to valuable filtering instructions that improve result accuracy.
3Ease of operation
If users provide search queries using their own terms and expectations, then the search reflects user intent, but ambiguous terms lead to different interpretations and irrelevant results
Solution Approach 1:
The system implements feedback mechanisms that analyze user search behavior and adjust interpretation of ambiguous terms based on context, user preferences, and historical data. This feedback loop refines the precision of term interpretation while maintaining ease of use, as users continue to type naturally but receive more accurate results.
Solution Approach 2:
The system dynamically adapts the interpretation of search terms based on contextual factors, user profiles, and search history. Rather than using static interpretation rules, the system adjusts its understanding of ambiguous terms in real-time, improving precision without requiring users to learn specialized search syntax.
Data Source
AI summary
Techniques for providing user-influenced search results may be provided. The system can introduce an enhanced query language that allows users to reprogram a computer system responsible for providing search results. In an embodiment, the enhanced query language is generated by the system and exposed to users. In another embodiment, a query language is generated by the user and translated by the system to an enhanced query language.