Candidate Search Ranking by Activeness Scores
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Solution Overview
Problem
Conventional techniques for ranking candidate search results do not consider the candidates' level of job-seeking activity or interest, often matching recruiters with inactive or uninterested candidates, leading to suboptimal user experiences for both recruiters and candidates.
Innovation Solution
The system ranks candidate search results based on their activeness, using machine learning models to generate scores that reflect a candidate's level of activity and responsiveness on the platform, allowing recruiters to identify and engage with active and interested candidates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If candidates are ranked based on traditional criteria (skills, experience, education), then relevant candidates are identified, but inactive or uninterested candidates are still matched with recruiters, leading to suboptimal user experiences
Solution Approach 1:
The patent introduces a new parameter - activeness score - that measures candidate engagement behavior (profile views, message responses, application actions). This parameter is combined with traditional ranking criteria to create a composite ranking system that prioritizes both relevance and engagement, resolving the contradiction by adding a new dimension to the evaluation framework
2Quantity of substance
If recruiters search through large candidate pools without activeness filtering, then more candidates are available, but time and resources are wasted on inactive candidates
Solution Approach 1:
The patent segments the candidate pool into distinct groups based on activeness levels (highly active, moderately active, inactive). This segmentation allows recruiters to selectively engage with high-value candidates first while maintaining access to the full pool, thereby reducing time loss without compromising quantity
3Measurement precision
If the system ranks only by relevance to job requirements, then qualified candidates are found, but unresponsive candidates are prioritized, reducing recruiter efficiency
Solution Approach 1:
The patent merges two previously separate ranking dimensions - relevance assessment and activeness measurement - into a unified ranking system. The combined scoring mechanism integrates both qualification metrics and engagement behaviors, producing a single prioritized candidate list that simultaneously addresses quality and efficiency concerns
Data Source
AI summary
The disclosed embodiments provide a system for processing data. During operation, the system determines activity features for candidates that match parameters of a search from a moderator of an opportunity, wherein the activity features include an amount of interaction between a candidate and additional moderators and a frequency of visits by the candidate to a platform used to conduct the interaction between the candidate and the additional moderators. Next, the system applies a machine learning model to the activity features to produce activeness scores representing levels of activity of the candidates with respect to the platform. The system then generates a ranking of the candidates according to the activeness scores. Finally, the system outputs at least a portion of the ranking as a set of search results of the search.