Dynamic Domain Filter for Search Engine Result Accuracy
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Solution Overview
Problem
Current search engines fail to accurately filter search results based on user intent due to reliance on weak indicators and static user preferences, leading to irrelevant results despite matching query terms.
Innovation Solution
Implementing a dynamically adjustable domain filter using a Bloom filter approach with URL prefixes from annotations to minimize false positives and ensure relevance of search results to user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a static domain filter is used to filter search results, then the filtering process is simple and fast, but the filter accuracy is low and many irrelevant results are returned
Solution Approach 1:
The patent applies dynamics by making the filter size adjustable rather than static. The filter dynamically changes its size based on the query characteristics and user intent, allowing it to expand when more filtering is needed and contract when simplicity is sufficient. This resolves the contradiction by enabling the filter to adapt its complexity level to the specific situation.
Solution Approach 2:
The patent changes the parameter of filter size from a fixed value to a variable that can be adjusted based on query analysis. By modifying this key parameter dynamically, the system achieves both high accuracy when needed and operational simplicity when sufficient, resolving the contradiction between reliability and device complexity.
2Reliability
If a large filter size is used to ensure comprehensive filtering, then more search results are captured, but the false positive rate increases and filter error rises
Solution Approach 1:
The filter size is made dynamic rather than fixed at a large value. The system adjusts the filter size based on the specific query and user intent analysis, using larger sizes when comprehensive coverage is needed and smaller sizes when precision is prioritized. This resolves the contradiction by allowing the filter to optimize between coverage and error rate for each query.
Solution Approach 2:
The patent modifies the filter size parameter from a constant large value to a variable that can be tuned. By changing this parameter based on query characteristics and required precision, the system achieves high search result relevance while maintaining low filter error rates, resolving the contradiction between reliability and measurement precision.
3Productivity
If a small filter size is used to maintain fast processing, then the system is efficient, but many relevant results are missed and filter coverage is insufficient
Solution Approach 1:
The filter size dynamically adapts based on query requirements rather than being fixed at a small value for speed. When queries require comprehensive coverage, the filter expands to capture all relevant results. When queries are simple, the filter remains small to maintain fast processing. This resolves the contradiction between productivity and information loss.
Solution Approach 2:
The patent changes the filter size parameter from a fixed small value to an adjustable variable. By modifying this parameter based on query analysis and required coverage, the system achieves both efficient processing speed and comprehensive capture of relevant results, resolving the contradiction between productivity and loss of information.
4Measurement precision
If domain filter is dynamically adjusted to minimize error, then filter accuracy improves, but computational overhead and system complexity increase
Solution Approach 1:
The system dynamically adjusts filter size based on query characteristics and error analysis. This dynamic behavior enables the system to minimize filter errors by adapting to different query scenarios, while the adjustment mechanism itself remains relatively simple, resolving the contradiction between measurement precision and device complexity.
Data Source
AI summary
In an embodiment, the size of a filter in a search engine is dynamically adjusted in order to reduce filter error. The filter, for example, may be a domain filter that contains domain(s) of preferred search results. A search engine processes the query and the domain filter to create a set of search results that are relevant to the query terms and for which a selected portion of the search results satisfy the domain filter, in that such portion of the documents comes from domains in the filter. The search results may then be provided to the user, as a set of search results that are relevant to the query terms and which include some portion of results that match the query labels. The construction of the domain filter may be done with a Bloom filter approach, using domain prefixes from annotations.