Entity-Aware Job Search Ranking via Semantic Matching
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Solution Overview
Problem
Traditional keyword-based job search systems fail to provide relevant and personalized results due to imprecise job title queries, inability to capture nuanced skill requirements, and limitations in handling dynamic document sets, leading to false positives and irrelevant job postings.
Innovation Solution
An entity-aware job search system that extracts and standardizes entities like job title, company, and location from user queries and job postings, using machine learning to construct semantic features and leverage member profiles for expertise homophily, enabling personalized ranking of job search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If keyword-based matching is used for job search, then the system is simple to operate and processes queries quickly, but the search results contain false positives and irrelevant job postings due to imprecise matching
Solution Approach 1:
The patent introduces an intermediary layer between keyword input and job matching by extracting and standardizing entities (job titles, skills, locations, companies) from user queries. This intermediary processing transforms free-text keywords into structured entities that can be semantically matched with job postings, thereby maintaining ease of operation while significantly improving matching accuracy and reducing false positives
Solution Approach 2:
The patent replaces the mechanical keyword-matching system with a semantic matching system based on entity recognition and standardization. Instead of directly comparing keywords with job text, the system extracts entities, standardizes them against a knowledge base, and performs semantic matching, thereby improving precision without sacrificing the simplicity of free-text query input
2Measurement precision
If semantic search with structured representation is used, then job matching accuracy improves, but the system becomes complex and difficult for users to describe information needs
Solution Approach 1:
The patent implements self-service by automatically extracting and standardizing entities from user queries without requiring users to manually structure their information needs. The system autonomously identifies job titles, skills, locations, and companies in the free-text query, standardizes them against a knowledge base, and performs semantic matching, thereby achieving high accuracy while keeping the user interface simple and intuitive
Solution Approach 2:
The patent performs preliminary entity extraction and standardization on user queries before the matching process. By pre-processing the query to identify and standardize key entities (job titles, skills, locations, companies), the system prepares the query in a structured format that enables accurate semantic matching, thereby achieving high precision without requiring users to understand or use complex semantic structures
3Adaptability or versatility
If keyword-based search is used, then the system can handle dynamic document sets and scale to web-sized data, but it returns false positives due to overbroad job title queries
Solution Approach 1:
The patent introduces dynamics by maintaining a knowledge base of standardized entities (job titles, skills, locations, companies) that can be continuously updated and expanded. The entity extraction and standardization process dynamically adapts to new job postings and queries, allowing the system to handle dynamic document sets while improving precision through semantic matching against the evolving knowledge base
Solution Approach 2:
The patent segments the job search process into distinct stages: entity extraction from queries, entity standardization against a knowledge base, and semantic matching with job postings. This segmentation allows the system to handle dynamic document sets at scale while applying precision-improving entity standardization to each query, thereby reducing false positives without sacrificing adaptability
4Measurement precision
If entity extraction and standardization are applied to job search, then personalized and relevant results improve, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary entity extraction and standardization on user queries before the matching process, preparing queries in a structured format that enables efficient semantic matching. By pre-processing queries to identify and standardize key entities, the system reduces the computational complexity of the matching stage, thereby improving result relevance while minimizing additional processing time
Solution Approach 2:
The patent extracts only the most relevant entities (job titles, skills, locations, companies) from user queries rather than processing the entire query text. This selective extraction focuses computational resources on the most important matching features, thereby improving job search result relevance while keeping processing time and computational overhead minimal
Data Source
AI summary
In an example, a plurality of member profiles in a social networking service are obtained, each member profile identifying a member and listing one or more skills the corresponding member has explicitly added to the member profile, the one or more skills indicating a proficiency by the member in the corresponding skill. A members-skills matrix is formed, wherein each cell in the matrix is assigned a value based on whether the corresponding member has the corresponding skill. The dot product of the members matrix and the skills matrix is then computed and used to identify one or more latent skills of a first member of the social networking service. Then a first digitally stored member profile is augmented with the one or more latent skills by combining the one or more latent skills with explicit skills for purposes of one or more searches that utilize member skills as an input variable.