Job Search Query Classifier for Precision and Recall Tradeoff
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Solution Overview
Problem
Conventional job search systems face challenges in precision due to ambiguities in search queries, leading to a high volume of irrelevant results, as a term can have multiple meanings and be found in various contexts within job postings, resulting in low precision and increased processing demands.
Innovation Solution
A machine-trained classifier is used to determine whether to perform a title field search or a compound search, restricting queries to specific contexts to enhance precision by training on job application records and applying a probabilistic model that balances precision and recall, optimizing the search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a broad search is performed across all job posting fields, then recall is improved, but precision deteriorates due to irrelevant results
Solution Approach 1:
The system dynamically adjusts the search scope based on the query type. For title field queries, it restricts search to title fields only. For compound queries, it expands search to multiple fields including description, skills, and company. This dynamic adjustment resolves the contradiction by adapting the search breadth to the specific query intent.
Solution Approach 2:
The system changes the search parameter (search scope) based on the query classification. When a query is identified as a title field query, the search parameter is set to search only title fields. When identified as a compound query, the parameter changes to search multiple fields. This parameter change allows the system to optimize for precision or recall depending on query type.
2Measurement precision
If a title field only search is performed, then precision is improved, but recall deteriorates due to missing relevant results
Solution Approach 1:
The system dynamically switches between title field only search and compound search based on query classification. This dynamic behavior allows the system to maintain high precision for title-focused queries while preserving recall for broader job search queries, resolving the contradiction between precision and recall.
Solution Approach 2:
The search scope parameter is changed based on query type. For title field queries, the parameter restricts search to title fields to maximize precision. For compound queries, the parameter expands to include multiple fields to maintain recall. This conditional parameter change resolves the precision-recall tradeoff.
3Device complexity
If conventional search is used without query classification, then device complexity is reduced, but processing power increases due to handling all queries uniformly
Solution Approach 1:
The system segments queries into different types (title field queries vs. compound queries) using a classifier. This segmentation allows different processing strategies to be applied to different query types, reducing the overall processing power needed compared to handling all queries uniformly with a single broad search strategy.
Solution Approach 2:
A query classifier is introduced as an intermediary component between the user query and the search engine. This intermediary classifies queries and routes them to appropriate search strategies, reducing the processing burden on the search engine by pre-filtering and categorizing queries before full processing.
4Power
If query classification is implemented, then processing power is reduced, but device complexity increases due to the classifier
Solution Approach 1:
A lightweight query classifier is introduced as an intermediary that performs simple text classification before search. While this adds a component to the system, the classifier is designed to be computationally efficient, and the overall system complexity increase is offset by the significant reduction in processing power required for the search operations themselves.
Solution Approach 2:
The system segments the search process into classification and execution phases. The classifier segment handles query categorization with minimal complexity, while the execution segment benefits from reduced processing requirements due to targeted search strategies. This segmentation distributes complexity across different stages of the system.
Data Source
AI summary
Systems and methods for classifying job search queries for improved precision using a machine-trained classifier are provided. In example embodiments, a network system receives a job search query including one or more terms from a device of a user. The network system extracts one or more features from the job search query, whereby the one or more features are derived from the one or more terms. Based on the one or more features, a machine-learned model of the classifier determines whether to use a title field search process or a compound search process. Based on the determining, the network system formats the job search query into a corresponding machine-language format and performs the job search query to derive results. The network system causes presentation of the results on the device of the user.