How to build recommendation model based on resume and job description? How to build a model which will result in better recommendation of resumes based on the job description given?
I am familiar with bow or tfidf (n-grams) approach and then take a cosine similarity but I'm looking for a deep learning approach. I don't have any labelled data to evaluate.
Anything suggestions will be really appreciated.
If you want a DL approach, I recommend substituting the tf-idf by some kind of word embeddings.
For instance, you can take a pre-trained word embedding model, like glove, and average its outputs both in resume and job description, and then compute cosine similarity. However, I recommend to use a contextual word embedding (BERT-like), as the terms in resumes might be very dependent on the context.
The following article also introduces sentence-bert, which I think is very suited for your problem.