Semantic Match

Bullhorn Automation leverages AI to match up jobs and candidates. It evaluates the similarity between job postings and candidates by analyzing factors such as Title, Location, Category, Skills, Specialties, and Industries.

To ensure optimal performance of the Semantic Matching features, it's essential to use the following standard fields exactly as intended. Custom fields are not compatible with Semantic Matching, so adherence to these guidelines is crucial for achieving optimized results.

Required Standard Fields:

  • Job Title: The Title field on the job record.
  • Candidate Position Title: This should correspond to the occupation field in the ATS.
  • Location: Optimized for locations in the US, UK, Australia, and New Zealand.

Fields You May Need to Activate:

  • Skills
  • Specialties
  • Industries
  • Category

By using these fields correctly, you can maximize the effectiveness of Semantic Matching and enhance the relevance of your matches.

Example:  Bullhorn Automation's Machine Learning model is able to determine that a Job posting “Java Developer” is semantically closer to a Candidate with a position title of “Software Engineer”, than to a Candidate with a position title of “Real Estate Developer.”

Matching Features

AI Auto Match: Matching Candidates to Jobs

Available for Bullhorn AutomationEnterprise edition.

The AI Auto MatchClosed Uses AI to match Candidates to Jobs feature identifies the most suitable candidates for open job positions by following a structured process:

  1. Match on Zip: Candidates are initially matched based on their zip code to ensure proximity to the job location.
  2. Match on State + City: If a zip code match is not found, the system expands the search to include matches based on the candidate’s state and city.
    • If the Candidate does not have a Zip Code or a City and State listed they are filtered out.
  3. Filter out already matched Candidates (Automatch): Candidates who have already been matched to the job in question are filtered out.
  4. Filter out Negative Feedback (Automatch): Candidates with previous negative feedback related to similar roles are filtered out to ensure quality matches.

For candidates who meet the initial criteria, Automation then employs AI Sourcing to refine the list further by focusing on job titles. It also takes into account skills, specialties, and industries to conduct a more detailed analysis. If a candidate has a relevant job title, they are prioritized for matching. However, if a candidate lacks a job title, they are not matched to jobs, as the model relies on job title data to make the best matches.

AI Job Match: Matching Jobs to Candidates

Available for Bullhorn Automation Corporate edition.

The AI Job MatchClosed Uses AI to match Jobs to Candidates. function works in the reverse, finding suitable job opportunities for candidates by running through the following criteria:

  1. Match on Zip: The system begins by identifying jobs within the candidate’s zip code.
  2. Match on State + City: If a direct zip code match is unavailable, it considers jobs in the candidate’s state and city.
  3. Match on State: As a broader criterion, jobs are also matched based on the candidate’s state if more specific matches aren’t found.
    • If the Candidate does not have a Zip Code, City, or State listed they are filtered out.

When a candidate has a job title, Automation then employs AI Sourcing to further filter and rank job opportunities to find the best fit. If the candidate does not have a job title, they are instead matched to jobs based solely on distance, ensuring that they receive job notifications even without title-specific data.

Results

For both AI Auto Match and AI Job Match results are also filtered based off of the maximum distance and minimum score configured.

Results are presented in order of Score (highest match) or if there was no score, in the case of matching jobs to candidates, then by radius (closest to furthest).

Documentation in this Section