What is the relation between Top-k and mean Average precision?

Question

can anybody help me, please? I have found in one article this text: "The similarity between the network feature maps was calculated using the Euclidean distance. Afterward, the Top-k candidates were chosen to generate a ranked list of relevant image candidates according to the mAP, where k={5, 10, 25, 50, 100}" in order to evaluate the pattern spotting stage. My question is, in the word spotting task, if we have a set of query images that each one is described using a features vector, and then we want to carry out a retrieval stage, we will compare each query vector with all of candidates dataset and after that rank the matching results. If we want now to evaluate our accuracy' system, we will compute for example the mAP which compares the ranked list with the ground truth results file. But in the paragraph cited above have used another type of evaluation "Top-K" that I am not understanding!