Candidate Search Query Generation via Machine Learning Ranking
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Solution Overview
Problem
Traditional methods for searching social networks for ideal job candidates require manual entry of search terms, making it challenging for recruiters to translate job position criteria into effective queries, especially due to the dynamic nature of required skills and knowledge.
Innovation Solution
A system that automatically generates a search query based on the profiles of specified 'ideal' candidates, using attribute extraction, expertise scoring, and collaborative filtering to build a query that can be refined by recruiters, and ranks search results using machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual search term entry is used, then recruiters can control query precision, but the time required for candidate search increases significantly
Solution Approach 1:
The system performs preliminary action by automatically generating search queries and ranking results before the recruiter needs to conduct the search. The machine learning model pre-processes candidate data and generates optimized queries based on job descriptions, eliminating the need for recruiters to manually construct queries from scratch and significantly reducing search time while maintaining precision.
Solution Approach 2:
The search system performs self-service by automatically generating and optimizing search queries without requiring manual intervention. The machine learning model autonomously analyzes job requirements, extracts relevant attributes, and constructs precise search queries, allowing the system to serve itself rather than requiring recruiter expertise and time investment.
2Loss of time
If automated query generation is used, then search time is reduced, but the complexity of the system increases
Solution Approach 1:
The system introduces an intermediary machine learning model that mediates between the job description input and the search query generation. This intermediary component handles the complex transformations required to convert unstructured job descriptions into structured search queries, abstracting the complexity from the user interface and allowing simple manual input to produce sophisticated automated results.
Solution Approach 2:
The patent replaces the mechanical manual process of query construction with an automated machine learning system. Instead of requiring recruiters to manually analyze and construct queries based on their expertise, the system uses computational models to automatically generate optimized queries, substituting human cognitive processes with automated algorithms that scale efficiently.
3Adaptability or versatility
If traditional manual searching is used, then recruiters can refine queries based on expertise, but the knowledge required to create effective queries varies and is difficult to maintain
Solution Approach 1:
The system implements feedback mechanisms where the machine learning model continuously learns from recruiter interactions and search outcomes. By analyzing which automated queries lead to successful candidate matches and how recruiters modify generated queries, the system refines its models over time, adapting to evolving job requirements and candidate pools without requiring recruiters to manually update their expertise.
Solution Approach 2:
The system handles parameter changes by using dynamic machine learning models that automatically adjust to varying job requirements and market conditions. Rather than requiring recruiters to manually update their knowledge base as skills and trends evolve, the system retrain its models on new data, automatically adapting query generation to current industry standards and candidate behaviors.
Data Source
AI summary
In an example embodiment, one or more sample ideal candidate member profiles in a social networking service are obtained, as well as one or more sample search result member profiles in the social networking service. Then, for each unique pair of sample ideal candidate member profile and sample search result member profile, a label is generated using a score generated from log information of the social networking service, the log information including records of communications between a searcher and members of the social networking service, the score being higher if the searcher communicated with both the member corresponding sample ideal candidate member profile and the member corresponding to the sample search result member profile in a same search session. The generated labels are fed into a machine learning algorithm to train a combined ranking model used to output ranking scores for search result member profiles.