Computing similarity scores
In order to build a recommendation system, it is important to understand how to compare various objects in our dataset. Let's say our dataset consists of people and their various movie preferences. In order to recommend something, we need to understand how to compare any two people with each other. This is where the similarity score becomes very important. The similarity score gives us an idea of how similar two objects are.
There are two scores that are used frequently in this domain -- Euclidean score and Pearson score. Euclidean score uses the Euclidean distance between two data points to compute the score. If you need a quick refresher on how Euclidean distance is computed, you can go to https://en.wikipedia.org/wiki/Euclidean_distance . The value of the Euclidean distance can be unbounded. Hence we take this value and convert it in a way that the Euclidean score ranges from 0 to 1. If the Euclidean distance between two objects is large, then the Euclidean...
