Normally, I am familiar with precision and recall evaluation metrics but as you know recall@k and precision@k are different things and used in ranking evaluations especially recommendation systems.
I checked many sources, I understood everything I could not understand a point.
One more thing,
Every source is different between each other in terms of calculation ( 1 , 2, 3, 4 )
let's get this example
I'll give you an example so you can explain to me
Let's say we have 5 users. We are trying to give location recommendations for the next visit to each user. We are analyzing users' historical check-in data and we are giving recommendation for the next visit.
User 1 is visiting: Museum1, Park1, Night Club, "?" (What is next)
We are trying to find the next visiting locations. Let's say our ground truth "Restaurant"
How can I calculate precision@5 and recall@5?
Extra: This youtube video is explaining very good (go to 51:45 on video)
What is 5-6 relevant item means? If we are giving recommendation it should be just 1 item that is gonna be relevant for the user. They are trying to make a movie recommendation but they are saying we have 5 relevant movies. What is that mean?