An Inside Look at the Components of a Recommendation Engine by Carol McDonald.
From the post:
Recommendation engines help narrow your choices to those that best meet your particular needs. In this post, we’re going to take a closer look at how all the different components of a recommendation engine work together. We’re going to use collaborative filtering on movie ratings data to recommend movies. The key components are a collaborative filtering algorithm in Apache Mahout to build and train a machine learning model, and search technology from Elasticsearch to simplify deployment of the recommender.
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There are two reasons to read this post:
First, you really don’t know how recommendation engines work. Well, better late than never.
Second, you want an example of how to write an excellent explanation of recommendation engines, hopefully to replicate it for other software.
This is an example of an excellent explanation of recommendation engines but whether you can replicate it for other software remains to be seen. 😉
Still, reading excellent explanations is a first step towards authoring excellent explanations.
Good luck!