问题描述
我收集了Text文档(在Node.js中)，其中一个文档i表示为单词列表.考虑到新文档是作为一种文档流出现的，计算这些文档之间相似度的有效方法是什么?
I gather Text documents (in Node.js) where one document i is represented as a list of words.What is an efficient way to compute the similarity between these documents, taking into account that new documents are coming as a sort of stream of documents?
我目前在每个文档中单词的归一化频率上使用cos相似度.由于可伸缩性问题，我不使用TF-IDF(术语频率，反文档频率)，因为我收到的文档越来越多.
I currently use cos-similarity on the Normalized Frequency of the words within each document. I don't use the TF-IDF (Term frequency, Inverse document frequency) because of the scalability issue since I get more and more documents.
我的第一个版本是从当前可用的文档开始，计算一个大的Term-Document矩阵A，然后计算S = A^T x A，以使S(i, j)为(在通过norm(doc(i))和norm(doc(j))归一化之后)单词频率分别为doc(i)和doc(j)的文档i和j之间的cos相似性.
My first version was to start with the currently available documents, compute a big Term-Document matrix A, and then compute S = A^T x A so that S(i, j) is (after normalization by both norm(doc(i)) and norm(doc(j))) the cos-similarity between documents i and j whose word frequencies are respectively doc(i) and doc(j).
获得新文档doc(k)时该怎么办?好吧，我必须计算该文档与之前所有文档的相似度，而无需构建整个矩阵.我可以将doc(k) dot doc(j)的内积用于所有以前的j，结果得到S(k, j)，这很棒.
What do I do when I get a new document doc(k)? Well, I have to compute the similarity of this document with all the previous ones, which doesn't require to build a whole matrix. I can just take the inner-product of doc(k) dot doc(j) for all previous j, and that result in S(k, j), which is great.
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在Node.js中计算
S真的很长.其实太久了！因此，我决定创建一个C ++模块，该模块可以更快地完成整个过程.确实如此！但是我等不及了，我应该能够使用中间结果.而我的意思是不等它"
Computing
Sin Node.js is really long. Way too long in fact! So I decided to create a C++ module which would do the whole thing much faster. And it does! But I cannot wait for it, I should be able to use intermediate results. And what I mean by "not wait for it" is both
a.等待计算完成，而且
b.等待矩阵A生成(这是一个很大的).
a. wait for the computation to be done, but also
b. wait for the matrix A to be built (it's a big one).
计算新的S(k, j)可以利用以下事实:文档的单词比所有给定单词的集合(我用来构建整个矩阵A)的单词少.因此，在Node.js中执行此操作看起来更快，从而避免了占用大量额外资源来访问数据.
Computing new S(k, j) can take advantage of the fact that documents have way less words than the set of all the given words (which I use to build the whole matrix A). Thus, it looks faster to do it in Node.js, avoiding a lot of extra-resource to be taken to access the data.
但是还有什么更好的方法吗?
But is there any better way to do that?
注意:我开始计算S的原因是，我可以在可以访问所有数据的Node.js中轻松构建A，然后在C ++中进行矩阵乘法并将其放回Node.js中，这可以大大加快整个过程.但是，现在计算S变得不可行了，它看起来毫无用处.
Note : the reason I started computing S is that I can easily build A in Node.js where I have access to all the data, and then do the matrix multiplication in C++ and get it back in Node.js, which speeds the whole thing a lot. But now that computing S gets impracticable, it looks useless.
注释2 :是的，我不必计算整个S，我只可以计算右上角的元素(或左下角的元素)，但这不是问题.时间计算问题不是这个顺序.
Note 2 : yep, I don't have to compute the whole S, I can just compute the upper-right elements (or the lower-left ones), but that's not the issue. The time computation issue is not of that order.
推荐答案
如果今天必须解决它，只需使用来自fasttext或word2vec的预训练单词向量
If one has to solve it today, just use pre-trained word vectors from fasttext or word2vec
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