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Automated Matching of CVs to Job Vacancy Descriptions (and vice versa)

CThis is an application of document similarity analysis based on the Aspire Content Processing platform.

In the recruitment industry, and in the HR departments of large companies, the Holy Grail of search applications is accurate, automatic comparison of job vacancies and CVs. The process is bi-directional and works simply like this:

  • A job description (the document as a whole) is submitted as the search request, and the comparison system returns a short list of the best-qualified candidates from a database of CVs.
  • Alternatively, a job seeker (or professional recruiter) submits a CV, and the system returns a list of the most appropriate, currently available vacancies.

OVERALL AUTOMATED CV/RESUME MATCHING APPROACH

As with a number of search-related applications, including near-duplicate detection, and document clustering, the usual approach is to extract document vectors for comparison purposes. With CVs and job descriptions, document vectorization is a multi-dimensional task. Dimensions include:

  • Job titles
  • Skills, experience and qualifications
  • Location (how close, geographically, is the candidate to the job)
  • Salary range
  • Industry sector

A good match will generally find a weighted balance of these vectors.

Read about how our CV/resume matching solution helped Adecco increase fill rates and reduce fill time.

TWEAKING THE VARIABLES

The need to take multiple dimensions into account necessitates a relevancy benchmarking system. Depending on the circumstances, document vectors can be weighted differently. For example:

  • For one job vacancy, proximity to the employer's location might be extremely important, but experience in the role is less important
  • In contrast, another job could require specific skills and experience, and given the salary expectations, and scarcity of those skills, candidate location is less important

A relevancy benchmarking system can be used to fine-tune the underlying algorithm, in terms of the contribution to the overall match-score made by the various document vectors. Relevancy benchmarking should always be based on human judgment, but much of the process can be automated.

On top of this, users can be given direct control over the weighting of vectors through a dynamic user interface, so that the specific requirements of a vacancy can be reflected in the search results.

Search Technologies provides custom Search & Match solutions for recruitment professionals, using leading commercial search products, or open source alternatives.

Contact us to see how this solution can help your recruiting company improve recruiters' productivity and the bottom line.

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