Part of the norms calculation at the index time on is similarity. Lucene has already implemented a complex model called TFIDFSimilarity as a default calculation for norms; you can read more about it on Lucene's website. In this section, we will talk about how we can tune similarity to suit our needs.

We will go through a similar scenario as we used in our example in norms. Instead of using boost to influence relevancy, we will leverage a NumericDocValuesField called ranking that will act as our boost. We will show you how to pull NumericDocValues at a query time within a Similarity class and how we can use it to influence score. This exercise will give you an idea of what you can do with similarity customization.