Item-to-Item recommendation using DNN
To be able to compare strings / Words or documents the data needs to be converted to a format the computer understand, vectors.
Google has a nice guide on Universal Sentence Encoder for sentence similarity that you can follow which explains how to generate a vector from the neural network that they have already trained.
What they do and what you can try is to use cosine similarity to compare the vectors. Compare one sentence vector with all other vectors and find the closest.
I am adding Annoy here, because I find it quite intuitive to use and you can get the n closest items.