Document Ranking Feature Generation via Query Similarity
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Solution Overview
Problem
Existing search engine ranking algorithms face challenges in accurately ranking documents when certain features are missing, leading to suboptimal results and increased user interaction, as they rely on past user interactions and similarity between queries to generate feature values for documents.
Innovation Solution
A method and system that utilize a machine learning algorithm to generate feature values for documents by retrieving and analyzing past queries from a search log database, determining similarity parameters between queries, and assigning values based on user interactions and relevance scores to improve document ranking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the search engine relies on past user interactions and similarity between queries to generate feature values for documents, then the accuracy of document ranking is improved, but the computational complexity and time required for processing increase
Solution Approach 1:
The system pre-computes and stores feature values for documents in a database before they are needed for ranking. When a query is received, the system retrieves pre-computed feature values instead of calculating them in real-time, significantly reducing computational complexity during query processing while maintaining ranking accuracy
Solution Approach 2:
The system creates copies of document feature values from past queries and user interactions, storing them in a database. These copied feature values are then reused for similar queries, avoiding redundant computations and reducing processing time and complexity
2Loss of information
If the search engine retrieves and analyzes past queries from a search log database to generate missing feature values, then the completeness of document features is improved, but the loss of time for processing queries increases
Solution Approach 1:
The system pre-retrieves and stores feature values from past queries in a database during idle periods or batch processing. When a query needs missing feature values, the system quickly retrieves pre-stored values rather than querying the search log database in real-time, reducing query processing time while maintaining feature completeness
Solution Approach 2:
The system extracts only the necessary feature values from past queries and user interactions, storing them separately in a database. This selective extraction allows the system to retrieve only required features during query processing, minimizing time loss while ensuring completeness of needed document features
3Reliability
If the machine learning algorithm uses multiple features including user interactions and query similarity to rank documents, then the relevancy of search results is improved, but the device complexity increases
Solution Approach 1:
The system segments the ranking process into separate components: document retrieval, feature value retrieval from database, similarity calculation, and final ranking. Each component handles a specific task independently, reducing overall system complexity while maintaining the ability to use multiple features for improved search result relevancy
Solution Approach 2:
The system introduces a database as an intermediary layer between the query processing system and the search log database. This intermediary stores pre-computed feature values and simplifies the interaction between components, reducing system complexity while enabling the use of multiple features for accurate document ranking
Data Source
AI summary
A method and a system for ranking a document in response to a query, the document having no value for a given feature with respect to the query. A set of documents relevant to the query is generated. The document is selected, and a set of past queries having presented the document as a search result are retrieved. Respective values for the given feature for the document with respect to the set of past queries are retrieved. A respective similarity parameter is determined between the query and each of the set of past queries. The value of the given feature for the document is generated based at least in part on the respective similarity parameter and the respective value for the given feature of at least one past query. The set of documents including the document is ranked based in part on the given feature.