January 2017
Beginner to intermediate
446 pages
8h 46m
English
Content preview from Artificial Intelligence with PythonBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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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 ...
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