The vectors are not normalized. That is: from __future__ import print_function from collections import Counter def cosine_similarity_unweighted(a,

The vectors are not normalized. That is: from __future__ import print_function from collections import Counter def cosine_similarity_unweighted(a, b): a = Counter(a) b = Counter(b) dotProduct = sum((c in a) * (c in b) for c in a) magnitudeA = sum(1 for c in a) magnitudeB = sum(1 for c in b) return dotProduct / (magnitudeA * magnitudeB)**0.5 def cosine_similarity_weighted(a, b): a = Counter(a) b = Counter(b) dotProduct = sum(a.get(c, 0) * b.get(c, 0) for c in a) magnitudeA = sum(value**2 for value in a.values()) magnitudeB = sum(value**2 for value in b.values()) return dotProduct / (magnitudeA * magnitudeB)**0.5 print("Scores for 'hello' and 'holl'") print("unweighted", cosine_similarity_unweighted("hello", "holl")) print("weighted", cosine_similarity_weighted("hello", "holl")) gives the output: Scores for 'hello' and 'holl' unweighted 0.866025403784 weighted 0.925820099773 The article gives: holl index: 0.9258200997725514 which means the article used letter weights. That said, the scores for your example are the same, because each of the letters in 'house' is used only once (the 3 in the numerator cancels out the 3 in the denominator): Scores for 'house' and 'hiusehiusehiuse' unweighted 0.8 weighted 0.8 If the query had instead been 'houss', you would see a difference in the similarities: Scores for 'houss' and 'hiusehiusehiuse' unweighted 0.67082039325 weighted 0.676123403783