Search Query Optionalization Rule Evaluation
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Solution Overview
Problem
Existing search engine technologies lack effective methods to evaluate and refine optionalization rules based on user interactions, leading to suboptimal search result quality.
Innovation Solution
Implementing click and skip evaluation methods to assess the impact of optionalization rules by incrementing click and skip counts based on user selections and rankings, and assigning scores to these rules using weights to determine their relevance and adjust their usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If optionalization rules are applied to generate revised search queries, then search query flexibility is improved, but search result quality deteriorates due to inclusion of unhelpful results
Solution Approach 1:
The system implements feedback loops where user interactions (clicks and skips) with search results are continuously monitored. This feedback is used to evaluate optionalization rules and adjust their confidence scores, thereby improving search result quality while maintaining query flexibility. The feedback mechanism enables the system to learn from user behavior and refine its optionalization strategy over time.
Solution Approach 2:
The system dynamically changes the parameter of rule confidence scores based on evaluation metrics derived from user interactions. By adjusting the confidence scores of optionalization rules according to their performance (click-through rates, skip rates), the system optimizes the balance between query flexibility and result quality, applying rules with higher confidence more aggressively while limiting low-confidence rules.
2Reliability
If click and skip evaluation methods are implemented to assess optionalization rules, then search result quality is improved, but system complexity increases
Solution Approach 1:
The system performs self-evaluation of its optionalization rules by automatically analyzing user interaction data (clicks and skips). Rather than requiring external manual evaluation, the system autonomously assesses rule performance and adjusts confidence scores based on observed user behavior, thereby improving search quality without proportionally increasing operational complexity.
Solution Approach 2:
The patent introduces an intermediary evaluation layer that sits between the optionalization rule application and the final search results. This intermediary component analyzes user interactions and mediates rule confidence adjustments, providing a structured approach to evaluating rules without fundamentally redesigning the entire search system architecture.
3Measurement precision
If low-confidence optionalization rules are adjusted based on evaluation scores, then search precision is improved, but loss of information increases due to removal of potentially useful rules
Solution Approach 1:
The system applies partial action by adjusting confidence scores rather than completely removing low-confidence rules. This approach allows the system to de-emphasize potentially problematic rules while retaining them in the rule set, preventing loss of potentially useful search coverage. The nuanced adjustment of confidence levels enables selective application without binary inclusion/exclusion decisions.
Solution Approach 2:
The system provides a cushioning mechanism by maintaining low-confidence rules in the rule set with reduced confidence scores rather than removing them entirely. This precautionary approach ensures that if such rules prove valuable under certain conditions, they remain available for application, thereby preventing information loss while still improving precision through confidence-based filtering.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for click or skip evaluation of optionalization rules. In one aspect, a method includes accessing query log data that identifies, for an initial search query, one or more revised search queries that were identified by designating a query term of the initial search query as optional according to one or more optionalization rules, and a particular search result selected by the user. A determination is made, using the query log data, that the particular search result corresponds to the one or more revised search queries that were identified by designating the query term of the initial search query as optional according to one or more optionalization rules. In response to this determination, a click count is incremented for the one or more optionalization rules that corresponds to the particular search result.