Recommender Systems

I want to learn about graph based embeddings for recommendations. Any suggestions?
So far my knowledge about recommendation systems is from this course.

I want to understand how graph embeddings are made and how are they different from other nodes/ entities in the recommender systems.

Nope I doubt it. But I'm sure you can learn enough on your own. I remember there was a coursera specialisation : https://www.coursera.org/specializations/recommender-systems

This is a great course on recommenders systems that should give you most of the theoretical background: https://www.coursera.org/specializations/recommender-systems . It does not include code in python though, but some java course-specific framework.

To clarify, I was asking on a technical, not ethical level, about the metrics you use to optimize recommender algorithms. Meaning -- well, my frame of reference is Genetic Algorithms where you have a fitness function that evaluates potential candidate solutions. A higher score in the fitness function means a better solution.

When optimizing for engagement the fitness function is (superficially, naively) simple. Just, how much time did the user ultimately spend on the platform. My question is, if you are optimizing for 'good for humanity', what do you measure that you can wire into this function?

I have an interest in understanding this and am looking at: https://www.coursera.org/specializations/recommender-systems -- if anyone has a better suggestion, feel free!

so basically a recommender system.

this might help: https://www.coursera.org/specializations/recommender-systems#courses

What a solid plan but it's only suitable for master/phD students preparing/ongoing their study in Machine learning. For building just an app, it's an overkill, you waste a lot of time for the things you don't need.

I recommend a top-down approach with a practical course in Recommendation System
- Recommender Systems and Deep Learning in Python - Building Recommender Systems with Machine Learning and AI

If you really want a solid theoretical foundation of Recommendation System, try this Coursera specialization. Which math parts you don't understand (Matrix decomposition, factorization, etc.) can be easily lookup - Recommender Systems Specialization

There are some hands-on courses/book out there for you to consider