How can similarity values in recommendations systems such as Mahout be trusted?

Question

I have been playing around with Mahout recommendation system lately, and succeeded to make a simple recommendation system out of it. But it doesn't make sense to me, how are these similarity values calculated by math can be useful for a recommendation system? Especially in ItemBasedSimilarity? I can understand that 2 users can be similar to each other by the items that they like/view/purchase/rate, but how are 2 items similar to each other?

Answer 1

After doing some research, I found my answer here (link). The article only shows 2 examples of 2 metrics (Euclidean distance and cosine similarity) but it helped visualizing how the similarity values are computed, thus can be trusted.