Query Rewriting with ML Confidence Scoring for Search Accuracy
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Solution Overview
Problem
Current search engines face challenges in accurately predicting user intent due to misspellings and contextual differences, leading to sub-optimal search results, with existing spelling correction mechanisms having high error rates and modifying queries that do not need correction.
Innovation Solution
A machine learning mechanism is integrated into the search mechanism to generate confidence scores for query suggestions and determine whether and how to rewrite search queries, using a query suggestion server, feature generating mechanism, and machine learning mechanism to account for user intent, blending terms from original and suggested queries for improved relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a spelling correction mechanism is used to correct query misspellings, then the relevance of search results is improved, but the error rate increases and queries that do not need correction are modified
Solution Approach 1:
The system uses click-through data from users as feedback to train the machine learning model. When users click on or reject query suggestions, this behavior data is fed back to improve the model's accuracy in determining whether corrections are needed, thereby reducing false corrections while maintaining relevant result retrieval
Solution Approach 2:
The system performs preliminary analysis by generating multiple query suggestions and confidence scores before making any correction decision. The machine learning model evaluates whether correction is actually needed based on pre-computed features and confidence levels, preventing unnecessary modifications before they occur
2Adaptability or versatility
If multiple query suggestions are generated to handle misspellings and contextual variations, then the coverage of user intent is improved, but the complexity of the search mechanism increases
Solution Approach 1:
The machine learning model acts as an intermediary between the query suggestion generation and the final query rewriting decision. It takes multiple suggestions and confidence scores as input, processes them through learned patterns, and outputs a simplified decision about whether and how to rewrite the query, managing the complexity of handling multiple variations
Solution Approach 2:
The system changes parameters by generating multiple query suggestions with different confidence scores and using these as features for the machine learning model. By transforming the problem into a parameter-based evaluation framework, the system can handle diverse user intents without proportionally increasing operational complexity
3Measurement precision
If a machine learning mechanism is integrated to determine query rewriting, then the accuracy of user intent prediction is improved, but the computational resources required increase
Solution Approach 1:
The system applies partial machine learning processing by using the model only to make the final rewriting decision based on pre-computed confidence scores and features. The heavy lifting of query suggestion generation is done by existing algorithms, and the ML model only performs classification on top of that, reducing overall computational burden while maintaining high accuracy
Data Source
AI summary
Techniques for rewriting queries submitted to a query engine are provided. A query is submitted by a user and sent to a search mechanism. Based on the query, one or more query suggestions are generated. Features are generated based on the query and the query suggestions. Those features are input to a trained machine learning mechanism that generates a rewrite score. The rewrite score signifies a confidence score that indicates how confident the search mechanism is that the user intended to submit the original query. If the rewrite score is below a certain threshold, then the original query is rewritten to a second query. Results of executing the original query may be sent to the user along with a reference to the second query. Additionally or alternatively, results of executing the second query are sent to the user.