Junk Profile Detection for Search Result Ranking
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Solution Overview
Problem
Search engines often return 'junk' documents that contain little human-readable data or are automatically generated, which can frustrate users and waste time, as existing systems lack the ability to distinguish these from relevant documents.
Innovation Solution
A junk profile is created to identify and rank documents based on junk scores, using histograms and similarity metrics to differentiate between human-generated and automatically created content, with a two-layer neural network and junk scores being used to optimize search result rankings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If search engines return all matching documents, then the quantity of search results increases, but the relevance and quality of results deteriorates due to inclusion of junk documents
Solution Approach 1:
The system performs preliminary classification of documents into junk and non-junk categories before final search result ranking. Junk profiles are created in advance by analyzing document characteristics such as term frequency histograms, and these profiles are used to pre-filter or re-rank search results, ensuring that junk documents are identified and deprioritized before presentation to the user.
Solution Approach 2:
The system changes the ranking parameters by introducing junk scores based on histogram comparisons. Documents are evaluated using multiple parameters including term frequency distributions, and those with high similarity to junk document histograms receive penalty scores that adjust their ranking position, thereby improving result quality without reducing the total number of available results.
2Reliability
If search engines prioritize human-generated content, then the relevance of search results improves, but the complexity of distinguishing junk documents increases
Solution Approach 1:
The system creates simplified copies or representations of junk documents in the form of junk profiles that capture essential characteristics through histograms. Instead of analyzing entire documents in detail, the system compares compact histogram representations, which reduces computational complexity while maintaining the ability to distinguish junk from non-junk documents effectively.
Solution Approach 2:
The system transforms complex document analysis into simpler parameter comparisons by using term frequency histograms and other statistical features. This parameter transformation allows the system to evaluate document quality based on measurable metrics rather than requiring complex semantic analysis, thereby improving relevance while managing system complexity.
3Reliability
If search engines filter out junk documents, then the quality of search results improves, but the time required to process and analyze documents increases
Solution Approach 1:
The system performs preliminary analysis to create junk profiles by extracting key characteristics such as term frequency distributions from sample junk documents. This pre-computed information is stored and reused during search operations, allowing rapid comparison of new documents against established junk patterns without repeating the full analysis process, thereby reducing processing time while maintaining quality filtering.
Solution Approach 2:
The system uses compact histogram representations as simplified copies of document characteristics. These histograms capture essential features in a compressed form that enables rapid comparison and classification, significantly reducing the time required to evaluate documents during search operations while maintaining effective junk document identification.
4Measurement precision
If search engines use histogram comparison for junk detection, then the precision of junk identification improves, but the computational resources required increase
Solution Approach 1:
The system uses histograms as simplified copies or summaries of document content that capture essential statistical properties without requiring full document analysis. These compact representations enable efficient comparison operations that maintain high precision in junk document identification while significantly reducing computational resource requirements compared to analyzing complete documents.
Solution Approach 2:
The system transforms detailed document content into condensed parameter representations through histograms and other statistical measures. This parameter transformation maintains the precision needed for accurate junk detection by preserving key distributional characteristics while reducing the computational burden to manageable levels suitable for real-time search operations.
Data Source
AI summary
Embodiments are directed to ranking search results using a junk profile. For a given corpus of documents, one or more junk profiles may be created and maintained. The junk profile provides reference metrics to represent known junk documents. For example, a junk profile may comprise a dictionary of document data that is automatically inserted into documents created using a particular system or template. A junk profile may also comprise one or more representations (e.g., histograms) of a distribution of a particular junk variable for known junk documents. The junk profile provides a usable representation of known junk documents, and the present systems and methods employ the junk profile to predict the likelihood that documents in the corpus are junk. In embodiments, junk scores are calculated and used to rank such documents higher or lower in response to a search query.