title: Why use cosine similarity in Word2Vec when its trained using dot-product similarity
body: <p>According to several posts I found on stackoverflow (for instance this <a href="https://stackoverflow.com/questions/38423387/why-does-word2vec-use-cosine-similarity">Why does word2Vec use cosine similarity?</a>), it's common practice to calculate the cosine similarity between two word vectors after we have trained a word2vec (either CBOW or Skip-gram) model. However, this seems a little odd to me since the model is actually trained with dot-product as a similarity score. One evidence of this is that the norm of the word vectors we get after training are actually meaningful. So why is it that people still use cosine-similarity instead of dot-product when calculating the similarity between two words?</p>