Self-tuning Search Engine with Feedback Loops for Query Refinement
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Solution Overview
Problem
Current search engines rely on accurate user input and are ineffective when search queries are poorly written or do not accurately reflect the user's intent, leading to frustration and inefficient information retrieval.
Innovation Solution
A self-tuning, feedback-oriented search engine generates alternative candidate search queries through linguistic models to refine search terms, correct misspellings, and modify keywords, which are then processed to provide optimized search results, incorporating interaction scores and candidate scores to enhance result relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search engines rely on accurate user input and keywords, then search results can be precise when queries are well-formed, but search effectiveness deteriorates when queries are poorly written or do not accurately reflect user intent
Solution Approach 1:
The system performs preliminary actions by automatically generating and pre-processing candidate search queries before executing the actual search. Linguistic models create alternative query variants (correcting misspellings, adding terms, modifying keywords) in advance, so that when a user submits a query, the system already has prepared candidate queries ready for immediate processing, eliminating the need for users to manually refine their searches
Solution Approach 2:
The system incorporates feedback mechanisms by monitoring user interactions with search results and using this information to update candidate scores and interaction scores. This feedback loop allows the system to learn from user behavior patterns and improve its query generation and result selection algorithms over time, adapting to varying query qualities without requiring explicit user input
2Measurement precision
If search engines conduct multiple simultaneous searches with variant search terms, then search result relevance improves, but system complexity and processing time increase
Solution Approach 1:
The system segments the search process into distinct components: a linguistic model that generates candidate queries, a probability generator that assigns scores to candidates, and a blending module that assembles final results. This segmentation allows each component to specialize in specific tasks, making the overall complex process of multi-query searching more manageable and efficient
Solution Approach 2:
The system manages complexity by dynamically changing parameters such as the number of candidate queries generated, the depth of linguistic analysis performed, and the weighting of different search results based on user interactions and candidate scores. This allows the system to adjust processing intensity based on query characteristics and user needs, balancing result relevance with computational resources
3Ease of operation
If search engines automatically generate and process variant search queries, then search result accuracy improves without user intervention, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary query generation and processing in the background before presenting results to the user. Candidate queries are generated and scored in advance, and the system prepares multiple search result sets simultaneously, so that when the user receives results, the heavy computational work has already been completed, making the user experience fast and convenient
Solution Approach 2:
The system maintains continuous processing by running multiple search queries and updating results in parallel rather than sequentially. The probability generator continuously scores candidates and the blending module continuously assembles results from multiple sources, ensuring that the search process is ongoing and efficient, reducing total processing time while maintaining high result relevance
Data Source
AI summary
Embodiment described herein are directed to an enhanced search engine with multiple feedback loops for providing optimal search results that are responsive a user's search query. The user's search query is parsed, and based on the underlying terms, different linguistic models and refinement techniques generate alternative candidate search queries that may yield better results. Searches are performed for the original search query and the candidate search queries, and different scores are used to select the best search results to present to the user. Results making it onto the list, as well as the underlying candidate search query, linguistic model, or refinement technique for generating that search query, will have their corresponding scores updated to reflect their success of generating a search result. Scores are stored and used by future searches to come up with better results.