Train a model for unstructured data I am new to Machine Learning. I want to develop Curriculum Vitae recommender system. I want to determine how similar 2 CVs are, and given a random CV, it suggest which cluster of CVs it belongs to?

This is what I've already done, following a blog post:


I have a folder containing lot of CVs or resume text documents in plain text format (.txt). 
I have done pre-processing on this data, like tokenization, stop words removal, stemming. 
I extracted the Candidate's name, email-id, contact number, education and experience.


I am confused with how to train the data and how do I create a model for that. More specifically, I have the following questions:


Now how to create a model on text data?
Which algorithm I should apply on this data?


Please anyone answer. Your help will be appreciated.

Thanks.
I have worked on a similar project with JDs, we basically created a word2vec model for words in JD, the result were good as we had lots of JDs. 
Basically, what word2vec does is convert a word to vectorial representation which signifies context. 
You can check the documentation here: https://radimrehurek.com/gensim/

You may extract skills or other stuff from CVs, and do a semantic similarity based on w2v model. You may use a custom formulae for comparing similarity. Other things could be education, experience, similar projects etc