Document similarity: Vector embedding versus BoW performance? I have a collection of documents, where each document is rapidly growing with time. The task is to find similar documents at any fixed time. I have two potential approaches:


A vector embedding (word2vec, GloVe or fasttext), averaging over word vectors in a document, and using cosine similarity.
tf-idf or its variations such as BM25.


Will one of these yield a significantly better result? Has someone done a quantitative comparison of tf-idf versus averaging word2vec for document similarity?

Is there another approach, that allows to dynamically refine the document's vectors as more text is added?
Here some points on which we can focus - 

1.) Averaging the words vectors lose the order of words, making it very similar to the concept of Bag of Words. That is why with less Data Bag of words approach perform better than word2vec.
it has been found that training word2vec with potentially large data , outperform Bag of Words models. Google's results are based on word vectors that were learned out of more than a billion-word corpus.

2.) Published literature, distributed word vector techniques have been shown to outperform Bag of Words models. In this paper, an algorithm called Paragraph Vector is used on the IMDB dataset to produce some of the most state-of-the-art results to date.

3.) In your case Document size increase with time. So a good approach would be not to cluster each word rather than clustering documents. Word2vec algorithm has power to learn embedding for each document , which we call Doc2vec. it will cluster similar documents. and using cosine similarity you can find similar documents.