TFIDF vs Word2Vec
In example one, the word2vec maybe doesn't have the words Bills and CHAPS into its bag of words. That been said, taking out these words the sentences are the same*. 
In Example 2, maybe in the tokenization of the word2vec algorithm, it took the "requirements:" as one token and the "requirements" as a different one, That's why their vector it's a bit different so they aren't exactly the same.
*Word2vec computes the sentence vector by taking the average of its word vectors. If a word arent in the word2vec's bag of words, it will have vector=[0,0,...0].
I am trying to find similarity score between two documents (containing around 15000 records).
I am using two methods in python:
1. TFIDF (Scikit learn) 2. Word2Vec (gensim, google pre-trained vectors)
Example1
Doc1- Click on "Bills" tab
Doc2- Click on "CHAPS" tab
First method gives 0.9 score.
Second method gives 1 score
Example2
Doc1- See following requirements:
Doc2- See following requirements
First method gives 1 score.
Second method gives 0.98 score
Can anyone tell me:
why in Example1 Word2Vec is giving 1 though they are very different
and in Example2 Word2Vec is giving 0.98 though they are having difference of only ":"