Query Performance Prediction via Bootstrap Sampling
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Solution Overview
Problem
Existing information retrieval systems face challenges in accurately predicting the relevance of search results to a query, particularly due to query drift, where irrelevant information dominates the result list, and the impact of query length on performance estimation.
Innovation Solution
A post-retrieval query performance prediction method that employs a standard deviation estimator using bootstrap sampling and weighted ranking schemes to simulate user behavior and mitigate query-dependent biases, normalizing performance measures to account for query representativeness and corpus relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional performance estimation methods are used, then the process is simple, but query drift causes irrelevant information to dominate and reduces measurement precision
Solution Approach 1:
The patent segments the result list into multiple sub-lists based on score thresholds, and further divides each sub-list into portions for sampling. This segmentation allows the system to analyze different score ranges separately, reducing the impact of query drift by focusing on high-score documents while maintaining a manageable analysis complexity through controlled sampling.
Solution Approach 2:
The patent applies partial action by sampling only a portion of documents from each sub-list rather than analyzing all documents. Specifically, it samples a predetermined number of documents from high-score sub-lists and a smaller number from lower-score sub-lists, achieving sufficient measurement precision without the excessive complexity of full-list analysis.
2Measurement precision
If the entire result list is analyzed, then measurement precision improves, but processing time and computational resources increase
Solution Approach 1:
The result list is divided into multiple sub-lists based on score thresholds, and each sub-list is further divided into portions. This segmentation enables selective sampling where only a fraction of documents from each sub-list are analyzed, significantly reducing processing time while maintaining measurement precision through strategic sampling of high-score documents.
Solution Approach 2:
The patent performs partial analysis by sampling a predetermined number of documents from each sub-list rather than analyzing the entire result list. This partial action approach achieves sufficient performance estimation accuracy by focusing computational resources on high-score documents that most influence the performance metric, thereby reducing overall processing time.
3Measurement precision
If uniform sampling is applied across all result lists, then ease of operation is maintained, but query-dependent biases reduce measurement precision
Solution Approach 1:
The patent applies different sampling strategies to different sub-lists based on their score characteristics. High-score sub-lists receive sampling of a predetermined number of documents, while lower-score sub-lists receive sampling of a smaller predetermined number. This local quality approach tailors the sampling intensity to the relevance of each sub-list, improving measurement precision by giving more weight to high-score documents while maintaining operational feasibility through standardized sampling procedures.
Solution Approach 2:
The patent changes the sampling parameter (number of documents sampled) based on the score threshold of each sub-list. By adjusting the sampling intensity according to the local characteristics of each sub-list, the system achieves query-independent performance estimation that is robust to query-dependent biases, while the parameter changes are implemented through systematic rules that maintain ease of operation.
Data Source
AI summary
A computer-implemented method, computerized apparatus and computer program product for query performance prediction, the method comprising: obtaining a result list comprising a listing of documents retrieved from a collection in response to a query; obtaining for each of the listed documents in the result list a score indicating a measure of the document's relevance to the query; sampling the result list to obtain a plurality of sub-lists each of which comprising a listing of documents subsumed by the result list; for each of the plurality of sub-lists, analyzing scores of the documents listed therein to obtain a sample performance estimator; and estimating performance of the result list based on the sample performance estimator of each of the plurality of sub-lists.