Also known as: Mean Average Precision At K (mAP@k) Β· mAP@k
Mean Average Precision at k, or mAP@k, is a ranking metric that evaluates the quality of the top k results returned by a system. It averages precision values across relevant retrieved items and then across queries, making it useful for search, recommendation, and retrieval evaluation.
The statistical mean of all average precision at k scores across a validation dataset. One use of mean average precision at k is to judge the quality of recommendations generated by a recommendation system. Although the phrase "mean average" sounds redundant, the name of the metric is appropriate. After all, this metric finds the mean of multiple average precision at k values. Suppose you build a recommendation system that generates a personalized list of recommended novels for each user. Based on feedback from selected users, you calculate the following five average precision at k scores (one score per user): - 0.73 - 0.77 - 0.67 - 0.82 - 0.76 The mean Average Precision at K is therefore: