One of the trends I've been seeing is the use of embeddings for similarity/recommendation in non-NLP domains (this talk and many others). While I've used/thought about word2vec for a while, I wanted to implement this to really get a sense of how it works and ways it can be extended to other domains. Intuitively, word2vec makes sense, and there are a lot of packages that will let you compute it without thinking much about the implementation (e.g. gensim
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