Semantic Job Title Matching via Word2vec Vectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current job search methods on social networks often miss valuable opportunities because they limit searches to specific parameters, failing to match users with job titles that are semantically similar, even if they are closely related.
Innovation Solution
The implementation of deep learning techniques, such as Word2vec, to perform semantic analysis and identify similarity coefficients among job titles, industries, and skills, expanding job search queries to include related terms and enhancing job recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the job search engine uses exact parameter matching for job titles, then the search precision is improved, but the adaptability to semantically similar job titles deteriorates
Solution Approach 1:
The patent transforms the job title matching parameter from exact string comparison to semantic similarity measurement using Word2vec vector representations. This allows the system to match job titles based on their semantic meaning rather than exact parameter equality, resolving the contradiction between precision and adaptability.
Solution Approach 2:
The patent replaces the mechanical exact-matching system with a semantic analysis system using deep learning models. Instead of direct string comparison, the system uses vector space semantics to determine job title similarity, enabling both precision and adaptability simultaneously.
2Adaptability or versatility
If the job search engine expands the search to include semantically similar job titles, then the adaptability is improved, but the search precision deteriorates
Solution Approach 1:
The system maintains precision by using similarity threshold parameters and weighted scoring mechanisms. Even when expanding to semantically similar titles, the system filters results based on similarity scores, ensuring that only sufficiently relevant matches are returned, thus maintaining precision while gaining adaptability.
Solution Approach 2:
The patent implements feedback mechanisms where the system learns from user interactions with job recommendations. This feedback refines the similarity calculations and filtering thresholds, allowing the system to maintain high precision while expanding adaptability to semantically similar titles.
3Device complexity
If the job search uses limited parameters, then the device complexity is reduced, but the loss of information about valuable job opportunities increases
Solution Approach 1:
The patent replaces simple parameter-based filtering with semantic analysis using Word2vec and deep learning models. This substitution enables the system to capture nuanced job opportunity information that would be lost in traditional parameter-based searches, while the modular architecture keeps complexity manageable.
Solution Approach 2:
The system performs preliminary semantic analysis and vector representation of job titles, skills, and industries in advance. This preprocessing creates a rich information foundation that reduces information loss during the actual job search process, while the pre-computed vectors reduce real-time computational complexity.
Data Source
AI summary
Methods, systems, and computer programs are presented for expanding a job search that includes an industry by adding other similar industries. A method identifies job titles of members in a social network and performs, utilizing a machine-learning program, semantic analysis of the job titles to identify similarity coefficients among the job titles. The machine-learning program utilizes social network data to identify the similarity coefficients. Further, the method includes an operation for receiving a job search query, from a first member, including a query job title, and for expanding the job search query with job titles that are similar to the query job title. The method further includes operations for executing the expanded job search query to generate a plurality of job results, and for causing presentation on a display of one or more of the top job results.