The books gives some examples about content based recommendation. An example of what I understood is at below.
A movie's attributes are values between $1$ and $10$. The duration attribute gets values between $1$ and $100$. If we use these raw values to calculate distance, duration will dominate purely because of wider range, so we should to normalize that value.
Standardization formulas usually cause values smaller than $1$. It may between $1$ and $-1$. But if I scale values between $1$ and $10$ so how this normalization can be right? I expect that $1$ duration value should be represent $1$ and $100$ duration value should be represent $10$. But as you have know standardization formula cause smaller than $1$. Why it is so?
Do I have to re-scale result for $1-10$ range again? For example if result is $0.43$ so that should represent $4$?