I am given a set of 10,000 journal articles, with their corresponding 100th-dimension embedded vectors. (The way they are embedded is unknown, but I'm guessing it is
word2vec. The vector values vary from -0.5 to 0.5.) Among the 10,000 article name data, 10 of them are my
target articles. My objective is to find several articles that are 'similar' to my
After reading this post, it seems like word similarity measurements such as
tf-idf are unnecessary for my task, for I already have an access to embedded vectors. If so, how would I calculate similarity between my
target articles and the rest optimally, given that my dataset is already massive (10,000 * 100)?