The large percentage value on the entries of the search result list and at the top of the side-by-side comparison is the overall score. A machine learning model has been trained on feedbacks by users rating how good a candidate’s profile fits the respective search request. It tries to predict how many stars an expert would give the fit between request and profile. The prediction is then scaled up to 0-100%.
In addition to this there are scores for competencies, projects, and certificates displayed on the entries of the search result list and the side-by-side comparison as well as for languages only on the side-by-side comparison. The language score corresponds to the fraction of requested languages that are in the profile of the candidate. So, if three languages are requested but the candidate only has one of these in his or her profile, then the score will be 33%. Likewise, the certificate score is the fraction of the requested certificates that are in the profile of the candidate and the project score provides the fraction of competencies and languages that are in at least one of the candidate’s projects.
For competencies, the machine learning model takes different indirect ontology relationships of these competencies into consideration. To account for this, the competency score is not calculated as a fraction of matching competencies but rather linked to the overall score calculated by the complex machine learning model and corrected for the results of the other three scores. This way the overall score can be seen as a combination of competency, language, certificates, and project scores, with the competency score accounting for the largest part of the overall score.