Machine Learning Sampling for Search Query Accuracy
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Solution Overview
Problem
Current content sampling methods, such as uniform random sampling and stratified sampling, are inadequate for handling queries with unknown or previously unseen distributions, leading to inaccurate results and inefficiencies in search engine operations.
Innovation Solution
The implementation of machine learning techniques to compute individual sampling probabilities for records in a database, allowing for non-uniform sampling that accounts for the uncertainty of query distributions, thereby improving the accuracy and efficiency of query operations on a sampled subset of the database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If uniform random sampling is used, then the sampling process is simple and fast, but the accuracy of query results deteriorates when query distributions are unknown or unseen
Solution Approach 1:
The patent changes the sampling probability parameter from uniform (constant) to non-uniform (variable) based on machine learning predictions. Each record is assigned a different sampling probability computed by a machine learning model that predicts query relevance, thereby improving accuracy while maintaining sampling efficiency through probabilistic selection.
Solution Approach 2:
The patent replaces the mechanical uniform random sampling mechanism with a machine learning-based probabilistic sampling system. The machine learning model computes individual sampling probabilities for records, substituting the simple uniform random selection with an intelligent system that adapts to query distributions and improves result accuracy.
2Measurement precision
If stratified sampling is used, then the sampling process accounts for known distributions, but it cannot handle unknown or previously unseen query distributions effectively
Solution Approach 1:
The patent makes the sampling process dynamic by using machine learning models that can adapt to different query distributions in real-time. Instead of fixed stratification based on known distributions, the system dynamically computes sampling probabilities based on predicted query relevance, enabling it to handle both known and unknown query distributions effectively.
Solution Approach 2:
The machine learning model enables the sampling system to self-adapt to unknown query distributions by learning from query patterns and automatically adjusting sampling probabilities. The system serves itself by using the machine learning model to identify important records without requiring manual stratification or prior knowledge of query distributions.
3Measurement precision
If a large subset of the database is sampled to improve accuracy, then the sampling accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent changes the sampling probability parameter to prioritize records with higher predicted relevance. By computing individual sampling probabilities based on machine learning predictions of query relevance, the system can achieve high accuracy with smaller sample sizes, reducing processing time while maintaining or improving result accuracy.
Solution Approach 2:
The patent extracts only the most relevant records for sampling by using machine learning to identify and prioritize records with high predicted query relevance. Instead of sampling uniformly or requiring large subsets, the system extracts a focused subset of high-value records that are most likely to contribute to accurate query results, reducing processing time while maintaining accuracy.
Data Source
AI summary
Briefly, embodiments disclosed herein relate generally to on-line content sampling, and more particularly to utilization of machine learning techniques to sample on-line content in a search engine environment, for example.