What is the MAP@K metric for?
What are you measuring? And where does it make sense to use it?
Unfortunately, I can't find much about this on the Internet. Could someone help me with this? Thanks in advance.
Topic neural metric neural-network
Category Data Science
MAP@k is normally used in recommendation systems, but also in other kinds of systems. Quoting from here:
If you have an algorithm that is returning a ranked ordering of items, each item is either hit or miss (like relevant vs. irrelevant search results) and items further down in the list are less likely to be used (like search results at the bottom of the page), then maybe MAP is the metric for you!
Some application examples are these Kaggle competitions:
And these are some resources with more information about it:
MAP@k stands for Mean Average Precision at cut off k. It is a performance metrics generally used in recommender systems. You can find the details about it the link below. http://sdsawtelle.github.io/blog/output/mean-average-precision-MAP-for-recommender-systems.html
Geeks Mental is a community that publishes articles and tutorials about Web, Android, Data Science, new techniques and Linux security.