I want to build an ai-system that helps recruiters to find the perfect match on candidate-profiles and jobdescriptions on an internal database.
I started to generate text-embeddings from both items and use a vectordatabase to find similar profiles. This works quite wells but cannot take all the parameters into account.
E.g. you find a match between a candidate from India and a job description on-site in Germany. The candidate won’t resettle to Germany.
Therefore I want to build a ML-Modell that generates matching suggestions based on tabular data.
Do you have any suggestion on an ML-Algorithm that works for this problem? I can create a trainingdataset with about 500 matches between jobdescription and candidate profile.
Next question is: how can I aggregate the outputs from both models into a shortlist of candidate suggestions?