Recommender Systems

Having faced a very similar problem evaluating a recommender system for a legal document search/suggestion engine (like Google News for lawyers), this gave me a proper "birds eye" perspective on that problem that I wish I had before.

I find myselt in a situation where I need to evaluate a recommender system I developed, and the topics and material discussed throughout the course gave me many insights.

Can not be recommended as a first and only introduction to a topic of an evaluation and metrics of recommender systems.

nice to learn excel statistic This course was very helpful for giving me a breadth of exposure to various ways to look at evaluating recommender systems.

We faced exactly the same problem you describe of finding the proper tradeoff between precision and recall, or search vs. discovery.BUT what is lacking here is teaching us how to go implement these different evaluation metrics in practice.

Loved the first part of the course where they introduced many relevant evaluation metrics (root mean square, Spearman, ROC, Precision/Recall, .etc).

If you had run us through problem set/assignments involving real-world situations like this, where we had to calculate these different metrics (given sample data) and come up with compelling cases for different metrics to use for evaluation, I would feel otherwise.That said thank you for your hard work putting the course/specialization together.