Patent ID: 11941517
Assignee: AMAZON TECHNOLOGIES, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 5:
6. A method, comprising:
selecting a plurality of machine learning tasks to predict a plurality of attributes of entities to train an entity representation generator for performing a downstream machine task;
training a multitask neural network (MNN) to perform the plurality of machine learning tasks, wherein the MNN comprises an encoder layer and a decoder layer including a plurality of decoders corresponding to the attributes, and the training comprises:
receiving, via the encoder layer, a time-ordered sequence of events associated with an entity, wherein the entity includes multiple members associated with a user account;
generating, via the encoder layer, a member composition of the user account that indicates classes of the multiple members based at least in part on the time-ordered sequence of events;
generating, via the encoder layer, a fixed-size representation of the entity based at least in part on the time-ordered sequence of events and the member composition of the user account; and
performing, via the decoders, the machine learning tasks based at least in part on the fixed-size representation of the entity to output the attributes of the entity;
wherein the training trains the encoder layer to encode signals of the plurality of attributes in fixed-size representations of entities; and

after the MNN is trained, storing the trained encoder layer as part of the entity representation generator; and
train a second machine learning model to perform the downstream machine learning task, wherein second machine learning model is trained using entity representations generated by the entity representation generator that includes the signals of the plurality of attributes associated with the plurality of machine learning tasks selected for the downstream machine learning task.