I was studying Cosine Similarity and I have just seen this article. https://medium.com/@rahulkuntala9/cosine-similarity-and-handling-categorical-variables-29f907951b5

The author uses Cosine Similarity in order to find the similarity between the p1 and the other vectors.

p1 = (1,0,0,150), newp1 = (1,0,0,100), newp2 = (1,0,0,200), newp3 = (0,0,1,135) and newp4 = (0,1,0,250)

Similarity(p1,newp1) = 0.999994

Similarity(p1,newp2) = 0.999998

Similarity(p1,newp3) = 0.99995

Similarity(p1,newp4) = 0.99994

My question is: Since I want the Cosine Similarity to be the weight to some values, how can I use these results in order to do that? All the similarities are almost 1 with no actual differences. I think that there is no reason to use these results. I have thought to use Euclidean Distance to find the similarity but I know that it is not the best method to find similarity.

What do you propose? Thank you!

  • $\begingroup$ Welcome to Math.SE! Please format your posts using MathJax. This page should give you a start at learning how to typeset mathematics here so that your posts say what you want them to, and also look good. $\endgroup$ – Brian Mar 28 at 12:12
  • $\begingroup$ That's a really bad example. There doesn't seem to be any reason to use cosine similarity in this case. $\endgroup$ – Rahul Mar 28 at 12:55
  • $\begingroup$ Impossible to give you any insight if you don't explain what you are comparing. These vectors are indeed all virtually multiples of $(0, 0, 0, 1)$ $\endgroup$ – Yves Daoust Mar 28 at 13:58
  • $\begingroup$ "I know that it is not the best method to find similarity": can you substantiate ? $\endgroup$ – Yves Daoust Mar 28 at 14:00
  • $\begingroup$ I used the example I have found in the article because it is close to what I am looking for. My data are computer performance metrics normalized to 1. My vectors are vectorA = [0.8, 0.75, 0.9] and vectorB = [0.85, 0.77, 0.83] and vectorC = [0.82, 0.72, 0.86]. So I have the same problem as I have mentioned in the post with the cosine similarity been almost 1. $\endgroup$ – christouandr7 Mar 28 at 15:30

Cosine similarity essentially measures the angle between two vectors.

If you think geometrically you can see why all your values are close to $1$. Consider the two vectors $(1,0,100)$ and $(0,1,150)$ in three dimensions. Each sticks up nearly vertical from the $x-y$ plane so tha angle between them is very small.

To separate the vectors in your application you have to find another way to take into account the differences in the first three categorical variables. There is no off-the-shelf formula for that.

If you deal with the categorical variables separately then perhaps the Euclidean distance will be reasonable for the others. It will see the large differences in values in the fourth coordinate.

Your answer will have to depend on what the variables actually mean in your context.


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