Methods and apparatuses for training prediction model

A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform one or more operations, comprising:

• obtaining a target sample, wherein the target sample comprises:
• a sample feature, a first label, and a second label, wherein a user corresponds to the target sample, wherein the first label indicates whether a target object is clicked on by the user, and wherein the second label indicates whether the user implements a target behavior related to the target object;
• performing model processing on the sample feature by using a prediction model, wherein the prediction model comprises a first branch and a second branch, wherein the first branch outputs a first probability that the target object is clicked on by the user, and wherein the second branch outputs a second probability that the user implements the target behavior;
• determining a first loss based on a first label value of the first label and the first probability;
• and
• when a predetermined condition is satisfied:
• determining a second loss based on a second label value of the second label and the second probability;
• and
• determining a predicted loss of the target sample based on the first loss and the second loss, wherein the predetermined condition comprises the first label value, which indicates that the target object is clicked on by the user;
• and
• training the prediction model based on the predicted loss.

A computer-implemented system, comprising:

• one or more computers;
• and
• one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations, comprising:
• obtaining a target sample, wherein the target sample comprises:
• a sample feature, a first label, and a second label, wherein a user corresponds to the target sample, wherein the first label indicates whether a target object is clicked on by the user, and wherein the second label indicates whether the user implements a target behavior related to the target object;
• performing model processing on the sample feature by using a prediction model, wherein the prediction model comprises a first branch and a second branch, wherein the first branch outputs a first probability that the target object is clicked on by the user, and wherein the second branch outputs a second probability that the user implements the target behavior;
• determining a first loss based on a first label value of the first label and the first probability;
• and
• when a predetermined condition is satisfied:
• determining a second loss based on a second label value of the second label and the second probability;
• and
• determining a predicted loss of the target sample based on the first loss and the second loss, wherein the predetermined condition comprises the first label value, which indicates that the target object is clicked on by the user;
• and
• training the prediction model based on the predicted loss.