Surrogate Document Feature Extraction for Search Ranking
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Solution Overview
Problem
Current information-retrieval systems face challenges in effectively leveraging query log data due to its noisy, sparse, and volatile nature, which hinders the accuracy of search result relevance.
Innovation Solution
The system analyzes query log data to generate surrogate documents, extracts features from these documents, and uses machine-learned ranking functions to improve the ranking of search results, incorporating user behavior and query similarity to enhance document retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If query log data is used directly for ranking documents, then user behavior information is utilized, but the data quality issues (noise, sparsity, incompleteness, volatility) reduce ranking accuracy
Solution Approach 1:
The query log data is segmented into multiple components: user queries, clicked documents, and user actions are separated and processed independently. Each component is analyzed to extract specific features (query features, document features, user behavior features) that are then combined to form comprehensive document rankings, thereby improving accuracy despite data quality issues
Solution Approach 2:
A surrogate document model is introduced as an intermediary between the raw query log data and the ranking system. This surrogate document contains extracted features that represent user behavior patterns without directly using the noisy raw data, thereby mediating between data utilization and ranking accuracy
2Quantity of substance
If more query log data is collected to improve ranking, then more user behavior information is available, but the noise and volatility in the data increase
Solution Approach 1:
Specific useful features are extracted from the query log data while leaving behind the noisy components. The system extracts query features, document features, and user behavior features through normalization and filtering processes, taking out only the signal while discarding the noise
Solution Approach 2:
The system converts the volatility and noise in query log data into beneficial signals by using aggregation and normalization techniques. User behavior patterns that appear volatile individually become meaningful when aggregated across multiple queries and users, transforming harmful variability into useful statistical signals
3Reliability
If query log data is normalized for space, punctuation, and syntax variations, then data quality improves, but processing complexity increases
Solution Approach 1:
The system applies parameter changes to the query log data by normalizing space, punctuation, and syntax variations. This transforms the raw data into a standardized format that improves quality and consistency, making the data more reliable for subsequent analysis and ranking operations
Data Source
AI summary
Systems and techniques are disclosed to rank documents by analyzing a query log generated by a search engine. The query log includes data relating to user behavior, queries and documents. The systems and techniques distill query log information into surrogate documents and extract features from these surrogate documents to rank the documents.