(EN) METHODS AND APPARATUSES FOR TRAINING USER BEHAVIOR PREDICTION MODEL ABSTRACT The disclosure provides methods and apparatuses for training a user behavior prediction model. A sample set generated through streaming is obtained, where any sample includes a sample feature, a first label, and a second label. A sample feature of each sample is input into the model, to obtain a prediction result about whether a corresponding user performs specific behavior. Each corresponding prediction loss is determined based on the prediction result and a first label value. A sample category that indicates a delay status and to which each sample belongs is determined based on the first label value and a second label value, and a weight value of each sample is determined based on the sample category. Weighted combination is performed on each prediction loss corresponding to each sample based on the weight value, and a parameter of the model is adjusted based on an obtained combined loss. Fig. 1 24