Query Refinement via Conditional Entropy Gain
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Solution Overview
Problem
Traditional querying methods for social networks to find suitable job candidates are inefficient, requiring recruiters to manually enter search terms and refine queries multiple times to identify qualified candidates, as the skills and qualifications required for positions evolve over time.
Innovation Solution
A system that automatically generates and refines search queries by suggesting facets such as title, company, industry, school, and location based on ideal candidate profiles, using machine learning to analyze and rank search results, thereby reducing the need for manual input and improving search efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recruiters manually create and refine search queries to find suitable candidates, then search precision can be improved, but the time and effort required increases significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating initial search queries based on job descriptions and candidate requirements before recruiters need to manually refine them. This pre-generation of queries with relevant facets and filters reduces the iterative refinement process that currently consumes significant time while maintaining search precision.
2Measurement precision
If recruiters perform multiple searching trials with various refinements to obtain satisfactory results, then candidate matching accuracy improves, but productivity decreases
Solution Approach 1:
The system implements feedback mechanisms that learn from recruiter interactions with search results. By analyzing which refinements lead to successful candidate matches, the system automatically adjusts and improves future query generations, thereby maintaining high candidate matching accuracy while reducing the number of trials needed and improving overall search productivity.
3Measurement precision
If recruiters manually understand and translate hiring criteria into search queries, then query relevance improves, but the complexity of the operation increases
Solution Approach 1:
The system enables self-service by automatically translating hiring criteria into optimized search queries without requiring recruiters to manually understand search syntax or perform iterative refinements. The system handles the complex translation process itself, maintaining query relevance while dramatically simplifying the operation for recruiters.
4Adaptability or versatility
If recruiters extensively refine search terms and filters to adapt to evolving skills requirements, then search adaptability improves, but the device complexity increases
Solution Approach 1:
The system applies dynamics by continuously adapting search queries based on evolving skills requirements and candidate profiles. Rather than requiring complex manual adjustments, the system dynamically updates search parameters, facets, and filters automatically, maintaining high adaptability while managing system complexity through automated processes.
Data Source
AI summary
In an example embodiment, a query for search results is received, the query including at least one value for one facet, a facet defining a categorical dimension for the search results. It is then determined that the facet in the query is exclusive. In response to the determination that the facet is exclusive: for each potential facet different from the facet in the query: for each potential value in the potential facet: conditional entropy gain of the value in the query and the potential value is determined. The potential value in the potential facet that has the highest conditional entropy gain is determined, as is the potential facet with the minimum maximum conditional entropy gain. Then the potential facet with the minimum maximum is input into a machine learning model, causing the machine learning model to output one or more suggested facets to add to the query.