Methods and apparatuses for training behavior prediction model

A computing system, comprising one or more storage devices one or more processors, and a computer program stored in the one or more storage devices, individually or collectively, wherein when the computer program is executed by the one or more processors, the computer program enables the one or more processors to, individually or collectively, conduct actions including:

• splitting a behavior sequence of a user to obtain multiple single-behavior sequences, each single-behavior sequence corresponding to a behavior and a time point of the behavior;
• performing time coding on the multiple single-behavior sequences to obtain multiple single-behavior time sequences of the multiple single-behavior sequences;
• inputting the multiple single-behavior time sequences into a behavior prediction model for the behavior prediction model to perform modeling of a time point for a single-behavior time sequence in the multiple single-behavior time sequences to obtain a distribution in time of behaviors corresponding to the multiple single-behavior time sequences;
• and
• training the behavior prediction model based on a first loss value between the distribution in time of the behaviors corresponding to the multiple single-behavior time sequences and the multiple single-behavior time sequences, the behavior prediction model configured to predict a time point and a type of at least one future behavior of the user based on the behavior sequence of the user.

A computer-readable storage medium having computer executable instructions stored thereon, the computer executable instructions, when executed by one or more processors, enabling the one or more processors to, individually or collectively, implement acts including:

• splitting a behavior sequence of a user to obtain multiple single-behavior sequences, each single-behavior sequence corresponding to a behavior and a time point of the behavior;
• performing time coding on the multiple single-behavior sequences to obtain multiple single-behavior time sequences of the multiple single-behavior sequences;
• inputting the multiple single-behavior time sequences into a behavior prediction model for the behavior prediction model to perform modeling of a time point for a single-behavior time sequence in the multiple single-behavior time sequences to obtain a distribution in time of behaviors corresponding to the multiple single-behavior time sequences;
• and
• training the behavior prediction model based on a first loss value between the distribution in time of the behaviors corresponding to the multiple single-behavior time sequences and the multiple single-behavior time sequences, the behavior prediction model configured to predict a time point and a type of at least one future behavior of the user based on the behavior sequence of the user.