CV (Curriculum Vitae) Recommender System using Machine Learning, Python, Apache Solr(Back-end), AngularJS (Front-end)
I guess, there are many ways, to recommend CVs, but here is what I would do:

I would use TF-IDF or LSI, so an unsupervised learning approach and I would use a corpus.

I would apply word stemming, create a dictionary containing unique word stems of the CVs, create a corpus containing word stem id from dictionary and word stem count for each word stem in each CV, so the document frequencies (DF) for all word stems, then create TF-IDF (term frequency inverse document frequency) model or an LSI (latent semantic indexing) model from dictionary and corpus. Then you have TF-IDF or LSI vectors for all CVs.

For matching and recommending, you apply word stemming to text input you want to match CVs with, calculate the LSI or TF-IDF vector for this input and match it with the most similar CV by using cosine similarity calculation.

Here and here you can find short Python code examples for the approach I described using LSI or TF-IDF.