Search Algorithm Feedback Training for Component-Level Result Quality
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Solution Overview
Problem
Conventional search technologies lack efficient mechanisms for merchants to provide specific feedback on search result quality, leading to labor-intensive and time-consuming processes that result in low-quality search results, which can harm both end-users and merchants.
Innovation Solution
A search algorithm management system that allows merchants to provide component-level feedback through proactive and reactive interfaces, enabling training of individual components such as spell check, NLP filtering, and direct response components using machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional submission forms or tickets are used for feedback, then merchants can provide feedback on search results, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent segments the search algorithm into multiple independent components (spell check, NLP filtering, direct response, ranking). This allows feedback to be targeted at specific components rather than requiring comprehensive submissions about entire search results, reducing the time and effort needed for both submission and processing while maintaining feedback effectiveness.
Solution Approach 2:
The system enables automated feedback collection and processing through machine learning models that automatically analyze search interactions and generate training data. This self-service mechanism eliminates manual submission requirements, significantly reducing both the labor intensity for merchants and the processing time for the search algorithm manager.
2Ease of operation
If general feedback submissions are used, then merchants can report issues, but the information lacks specificity about the nature of the quality issue
Solution Approach 1:
By dividing the search algorithm into distinct components (spell check, NLP filtering, direct response, ranking), the system enables merchants to provide feedback on specific component failures rather than general search quality issues. This segmentation allows for precise identification of which component produced the low-quality result, significantly improving measurement precision while maintaining ease of operation through targeted feedback forms.
Solution Approach 2:
The patent replaces manual, imprecise feedback mechanisms with automated machine learning analysis that precisely identifies which algorithm component failed and why. The system automatically traces low-quality search results back to specific components, substituting human judgment with automated diagnostic capabilities that provide exact, actionable information about the nature of quality issues.
3Reliability
If manual processing of feedback submissions is used, then the search algorithm manager can review feedback, but a large number of submissions require significant time to process and respond to
Solution Approach 1:
The system implements self-service through automated machine learning models that independently process feedback submissions, analyze component failures, and generate training data without human intervention. This automation maintains reliability by using consistent, objective analysis while dramatically increasing productivity by processing thousands of submissions simultaneously rather than sequentially through manual review.
Solution Approach 2:
The patent substitutes manual review processes with automated machine learning analysis that rapidly processes feedback submissions. The system automatically identifies patterns, determines which components need training, and generates training datasets, replacing the time-consuming manual review process with scalable automated processing that maintains or improves review quality through consistent application of training criteria.
4Measurement precision
If extensive processing time is spent on each submission, then the search algorithm manager can thoroughly analyze feedback, but low-quality search results continue to be produced during this time
Solution Approach 1:
The system performs preliminary action by automatically analyzing feedback submissions and generating training data in real-time, before manual review would complete the process. Machine learning models continuously process feedback and update component training, so remediation begins immediately rather than waiting for manual analysis to complete. This preliminary automated action reduces remediation delay while maintaining thorough analysis through the machine learning evaluation process.
Solution Approach 2:
The system uses self-service mechanisms where machine learning models automatically analyze feedback depth, identify component failures, and generate appropriate training data without requiring extensive manual processing time. This automated self-analysis maintains measurement precision through consistent evaluation criteria while eliminating the time delay associated with manual review, as the system continuously processes and acts on feedback autonomously.
Data Source
AI summary
A system and method to train one or more components of a search algorithm based on feedback received from a user system. The system and method provide, via an interface, a first search query comprising one or more terms and a first query response generated by a component of the search algorithm. The system and method receive, via the interface, a label corresponding to the query response. An override action is generated in view of the label. In response to a second query comprising the one or more terms, generating a second query response in accordance with the override action.