I am interested in calculating similarity between vectors, however this similarity has to be a number between 0 and 1. There are many questions concerning tf-idf and cosine similarity, all indicating that the value lies between 0 and 1. From Wikipedia:
In the case of information retrieval, the cosine similarity of two documents will range from 0 to 1, since the term frequencies (using tf–idf weights) cannot be negative. The angle between two term frequency vectors cannot be greater than 90°.
The peculiarity is that I wish to calculate the similarity between two vectors from two different word2vec models. These models have been aligned, though, so they should in fact represent their words in the same vector space. I can calculate the similarity between a word in
model_a and a word in
model_b like so
import gensim as gs from sklearn.metrics.pairwise import cosine_similarity model_a = gs.models.KeyedVectors.load_word2vec_format(model_a_path, binary=False) model_b = gs.models.KeyedVectors.load_word2vec_format(model_b_path, binary=False) vector_a = model_a[word_a].reshape(1, -1) vector_b = model_b[word_b].reshape(1, -1) sim = cosine_similarity(vector_a, vector_b).item(0)
sim is then a similarity metric in the [-1,1] range. Is there a scientifically sound way to map this to the [0,1] range? Intuitively I would think that something like
norm_sim = (sim + 1) / 2
is okay, but I'm not sure whether that is good practice with respect to the actual, mathematical meaning of cosine similarity. If not, are other similarity metrics advised?