Query Rewrite Rule Generation via Hidden Markov Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods have not effectively addressed the challenge of scaling intent-based search by efficiently populating and leveraging libraries of query rewrite rules, which are crucial for interpreting user queries and retrieving relevant information from heterogeneous data sources.
Innovation Solution
The use of a Hidden Markov Model (HMM) based approach to automatically discover and infer high-quality query rewrite rules from query logs and click-through data, allowing for the transformation of keyword queries into intent-based interpretations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to populate and leverage libraries of query rewrite rules, then the system can maintain simplicity in rule generation, but the system fails to effectively scale intent-based search and accurately interpret user queries
Solution Approach 1:
The system automatically generates query rewrite rules by analyzing query logs and click-through data without manual intervention. The learning module autonomously processes historical query data, identifies patterns in user behavior, and generates rewrite rules that capture user intent, enabling the system to self-improve its query interpretation capabilities while scaling to handle diverse search scenarios
Solution Approach 2:
The system utilizes click-through data from query logs as feedback to refine and generate query rewrite rules. By analyzing which search results users click on after submitting queries, the system learns to generate more accurate rewrite rules that better reflect actual user intent, creating a continuous improvement loop that enhances both accuracy and scalability
2Measurement precision
If auxiliary data-based approaches are used to interpret search queries through concepts, then the system can improve query interpretation, but the system struggles to effectively populate concept libraries and scale the approach
Solution Approach 1:
The system automatically populates and refines its concept library by analyzing query logs and click-through data without requiring manual curation. The learning module extracts concepts and their relationships directly from historical data, enabling the system to self-build its knowledge base while maintaining high query interpretation accuracy
Solution Approach 2:
The system pre-processes and analyzes query logs to build a comprehensive concept library before deployment. By performing preliminary analysis of historical query data to identify patterns, concepts, and user intent, the system prepares its knowledge base in advance, reducing the complexity of ongoing concept library maintenance while sustaining high interpretation accuracy
Data Source
AI summary
Methods and arrangements for conducting a search using query logs. A query log is consulted and query rewrite rules are learned automatically based on data in the query log. The learning includes obtaining click-through data present in the query log.