Gotta build on of these for a complex and content rich elearning site.

So did some research.

Here are some of my findings.

Here is a good start :

How to Build a Recommender System by Martin Kihn

The Components of a Recommender System on a site with quite some articles on the topic – like this one on Impression Discounting

Or this one :

Beginners Guide to learn about Content Based Recommender Engines which is from 2015 but still cover the general basis.

Then you have some actual building – for example here in R : A recommendation system in R, applied with respect to the movielens database by Nelson Manohar who seems to specialised in machine learning based on his online profile.

Then we have an old plugin writen for WooCommerce Perso Recommendation Engine Plugin for Woocommerce which is nice to see how it is integrated in WordPress.

Then on youtube, we have quite some tutorials providing some interesting insights with some practical applications – usually movie recommendation engines.

Here we have a good intro from Standford university:

Overview of Recommender Systems | Stanford University

Then finally we can do one :

A really short tutorial (40 min total) to make a python movies recommendation system :

Recommendation Systems – Learn Python for Data Science #3

And the same guy but different stack :

Build a Movie Recommender – Machine Learning for Hackers #4

Then a different approach, with more algorithm into it :

Coding Challenge #70.1: Nearest Neighbors Recommendation Engine – Part 1

Coding Challenge #70.2: Nearest Neighbors Recommendation Engine – Part 2

Coding Challenge #70.3: Nearest Neighbors Recommendation Engine – Part 3

Then a long one of an hour+ integrating with tensor flow (the google library for deep learning) : Recommendation Systems / Engines with TensorFlow – Google Cloud Platform User Group Singapore

Probably more to come, but that’s a good start for now.

Do not hesitate to suggest stuff – either in the comment or on social somewhere