You understood the power and flexibility of Elasticsearch to implement your own scoring logic in this chapter. This flexibility can be utilized for a variety of purposes, mainly in the e-commerce industry. A lot of intellectual properties can be built over this platform, which would be the core of many business logics.
Let's do a recap on how scoring can be done. It can be divided as follows:
A query-based score: This is the score returned by Elasticsearch on the text match. This is computed based on term frequency, inverse document frequency, and field length normalization. Under this section, you learned that you can also do the following:
You can boost a match on certain fields over others using the multi field option in the match query
You can boost certain queries over others in the bool query
Custom score based on user requirements: Using the function query, we can implement various functions that provide scores on their own. Later, the scores that are returned by various functions...