Multi-level model architecture for controlling the size of user behavior models

A method, comprising:

• performing, by a user behavior inference system implemented by one or more computer devices:
• executing a multi-level machine learning (MLML) model trained using one or more ML techniques to generate inferences about user behaviors, wherein the MLML model is organized in multiple levels according to a multi-level model architecture that includes an upper level and a lower level;
• receiving (a) a user feature vector encoding user features of a user and (b) a behavior feature vector encoding a behavior sequence of the user;
• inputting the user feature vector to the upper level of the MLML model, wherein the upper level processes the user feature vector to output operating parameters of the lower level without processing the behavior feature vector;
• and
• inputting the behavior feature vector to the lower level of the MLML model, wherein the lower level of the MLML model processes the behavior feature vector according to the control parameters produced by the upper level of the MLML model based on the user feature vector, to generate a behavior inference about the user.

One or more non-transitory computer-accessible storage media storing program instructions that when executed on one or more processors of user behavior inference system, cause the user behavior inference system to:

• execute a multi-level machine learning (MLML) model trained using one or more ML techniques to generate inferences about user behaviors, wherein the MLML model is organized in multiple levels according to a multi-level model architecture that includes an upper level and a lower level;
• receive (a) a user feature vector encoding user features of a user and (b) a behavior feature vector encoding a behavior sequence of the user;
• input the user feature vector to the upper level of the MLML model, wherein the upper level processes the user feature vector to output control parameters of the lower level without processing the behavior feature vector;
• and
• input the behavior feature vector to the lower level of the MLML model, wherein the lower level of the MLML model processes the behavior feature vector according to the control parameters produced by the upper level of the MLML model based on the user feature vector, to generate a behavior inference about the user.