Job and candidate search, job recommendations and automated candidate evaluations have one thing in common. They are a matching problem.

Simply put, given a set of CVs and a set of vacancies, the most similar items should match, that is, these items should come out at the top of the search, recommendation or evaluation. Most applications use either of two high-level approaches to achieve this: behavior-based or content-based. They each have pros and cons, and there are also ways to combine the approaches to take advantage of both techniques.

Behavior-based approaches leverage user behavior to generate recommendations or suggestions. These approaches are domain agnostic, meaning the same algorithms that work on music or movies can be applied to the jobs domain. Behavior-based approaches do suffer from a cold start problem. If you have little user activity, it is much harder to generate good quality results.

Content-based approaches use data, such as user preferences and features of the items being matched or recommended, to determine the best matches. For recommending jobs, using keywords of the job description to match keywords in a user’s resume is one content-based approach. Using keywords in a job to find other similar jobs is another way to implement content-based recommendations.

However, the issue in this process is really the determination of similarity between two items. How can the similarity between for instance a resume and a vacancy be determined effectively even though they are often structured extremely heterogeneously? All too often, simple keyword-based matching is used for this, which means that many similarities go undetected, as keyword variations, synonyms and alternative phrases are not matched. With a content-based approach, it is important that the semantics (the underlying meaning) of two items be compared rather than the wording. This is where ontologies come into play. They can provide a relational model that can detect the underlying meanings and similarities in CVs and job descriptions. Ontologies enable a digital representation of implicit knowledge: humans usually understand the correct meaning of a term, thanks to their background knowledge and the context in which a specific term is used. A machine on the other hand lacks this ability. It can however, learn about the semantic meaning of a term by means of the concepts and relations stored in an ontology. By using an occupation and skills ontology as an intermediary, content-based approaches for job recommendations, job and candidate search and automated candidate evaluations can achieve much more.

The comprehensive ontology of occupations and skills JANZZon! for example offers a large number of poly-directional concepts pertaining to the global labor market. With its extraodrinary range of concepts, this ontology offers essential context and intelligent evaluation and enhancement options for applications such as information systems, matching engines, job portals, CV parsers, statistical analysis and modelling tools.