Can anyone explain the intuition of recommendation API?

[failable]

I read the document of it, but not sure what's the intuition behind

The recommendation API allows specifying several positive and negative vector IDs, which the service will combine into a certain average vector.

average_vector = avg(positive_vectors) + ( avg(positive_vectors) - avg(negative_vectors) )

If there is only one positive ID provided - this request is equivalent to the regular search with vector of that point.

Vector components that have a greater value in a negative vector are penalized, and those that have a greater value in a positive vector, on the contrary, are amplified. This average vector will be used to find the most similar vectors in the collection.