I’m a newcomer to ML and I’m trying to solve the following problem:
I have a text data, namely a set of vacancies names with corresponding requirements descriptions (e.g.
[ML specialist]-->[Experience in python NLTK, advanced sql et.]). I would like to predict a set of appropriate vacancies for a person with an input set of his/her competences.
After doing some NLP operations I got a vocabulary of competencies in such a form:
['sql','java','python'...] consisting of all appearing in initial data sensible tokens and the same type vocabulary for vacancies names. Then I decided to encode any newcoming competence’s set such as binary string  (len=len of vocab) where 1 represents that specific competence appears in a set and 0 otherwise.
Now can I use Naive Bayes Classifier to predict suitable vacancies,using probabilities of each class(each vacancy) as a suitability degree? Mb you could advise some more fitting ML approaches?