# Create most "average" cosine similarity observation

For a recommendation system I'm using cosine similarity to compute similarities between items. However, for items with small amounts of data I'd like to bin them under a general "average" category (in the general not mathematical sense). To accomplish this I'm currently trying to create a synthetic observation to represent that middle of the road point.

So for example if these were my observations (rows are observations, cols are features):

[[0, 0, 0, 1, 1, 1, 0, 1, 0],
[1, 0, 1, 0, 0, 0, 1, 0, 0],
[1, 1, 1, 1, 0, 1, 0, 1, 1],
[0, 0, 1, 0, 0, 1, 0, 1, 0]]


A strategy where I'd simply take the actual average of all features across observations would generate a synthetic datapoint such as follows, which I'd then append to the matrix before doing the similarity calculation.

[ 0.5 ,  0.25,  0.75,  0.5 ,  0.25,  0.75,  0.25,  0.75,  0.25]


While this might work well with certain similarity metrics (e.g. L1 distance) I'm sure there are much better ways for cosine similarity. Though, at the moment, I'm having trouble reasoning my way through angles between lines in high dimensional space.

Any ideas?

• I didn't quite get your question. Do you intend to compute pairwise cosine similarities between every pair of row vectors (observations)? Jul 1 '14 at 18:37
• Moreover, I didn't quite get why are you taking the average? Jul 1 '14 at 18:37
• If your observations are just 0s and 1s, then I don't think cosine similarity will work. Perhaps you should consider using Tanimoto similarity, which considers similarity across bitmaps. Jul 2 '14 at 1:30
• @Debasis yes, I do plan to compute similarities between each row. And I'm only taking the average as an example strategy to show what my computations might involve. Jul 2 '14 at 3:30
• @buruzaemon my observations aren't actually 0s and 1s, though I do like the simplicity of those kind of similarities. I actually asked this question because I'm interested in what the right answer for cosine similarities is. But yeah, I do appreciate the advice. Jul 2 '14 at 3:35