[P] Optimizing recall for specified precision in multiclass problem • r/MachineLearning

In a task of an online recommender system we try to predict what user will buy next in a new order, based on the collective history from all the users. For over 12k products in 800k orders we have trained individual classifiers for each product (based on xgboost trees) that produce rank in range 0;1 (the higher the rank, the better). The data has obviously large class imbalance, top 500 products constitue 50% of all ordered products. The classifier ranks are concatenated together, and by thresholding over 12k outputs we get the products that should be recommended. The metric that we use is that we want to maximize recall for specified precision 0.5.