Query Term Proximity Rule Evaluation via Click Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Search engines face challenges in accurately evaluating query term proximity rules, leading to suboptimal search result rankings, as existing methods fail to effectively differentiate between relevant and irrelevant term separations based on user interactions.
Innovation Solution
Implement a system that evaluates query term proximity rules by using click and skip counts to determine the relevance of term separation, adjusting weights for click, skip, and fake skip counts to refine search query scoring, and dynamically revising search queries based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If query term proximity rules are applied to score search results, then search result relevance is improved, but false proximity rules may degrade search quality by penalizing legitimately separated terms
Solution Approach 1:
The system uses user interaction feedback (clicks and skips) to evaluate and refine proximity rules. By monitoring whether users click on or skip search results based on term proximity, the system automatically adjusts which proximity rules to apply, removing false rules that degrade search quality while maintaining those that improve relevance.
Solution Approach 2:
The system dynamically changes the parameters of proximity rule application based on observed user behavior. By adjusting the confidence levels and applicability thresholds of proximity rules according to click and skip data, the system optimizes the balance between enforcing term proximity and allowing legitimate separations.
2Measurement precision
If proximity rules are strictly enforced, then term relevance is improved, but user preference for flexible term separation is lost
Solution Approach 1:
The system makes proximity rule application dynamic rather than static. By continuously monitoring user clicks and skips, the system adapts which proximity rules are enforced and with what stringency, allowing flexible adjustment to user preferences while maintaining term relevance where appropriate.
Solution Approach 2:
The system applies different levels of proximity enforcement to different query term pairs based on local user behavior patterns. Instead of uniformly enforcing all proximity rules, it selectively applies them based on whether users demonstrate preference for proximity in specific term contexts, allowing local optimization for each term pair.
3Measurement precision
If click and skip evaluation is implemented, then proximity rule accuracy is improved, but system complexity increases
Solution Approach 1:
The system evaluates and refines its own proximity rules using user interaction data without requiring external manual evaluation. By automatically monitoring clicks and skips and using this feedback to remove false proximity rules, the system self-improves its accuracy while managing complexity through automation.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for click or skip evaluation of proximity rules. In one aspect, a method includes accessing query data that identifies, for a search query, a particular query term and other query terms included in the search query, search results, and a particular search result selected by a user. The method further includes determining, using the query data, that, (i) in text associated with the particular search result, the particular query term is separated from the other query terms by a minimum number of terms, and (ii) in text associated with a search result that was ranked higher than the particular search result, the particular query term is not separated from the other query terms by the minimum number of terms, then incrementing a click count for a query term proximity rule corresponding to the particular query term.