Low-dimensional neural-network-based entity representation

A system, comprising:

• one or more computers that implement a voice-controlled device, configured to:
• receive audio input from a group of users over a time period, wherein the audio input includes voice commands associated with different items over the time period;
• generate, based on the audio input and via an encoder model trained using one or more machine learning techniques, a fixed-size representation of the group of users, wherein the fixed-size representation indicates a member composition of the group based on user classes detected in the group;
• upload the fixed-size representation to a remote service that implements a machine learning model, wherein the machine learning model uses the fixed-size representation to generate a personalized output for the group of users;
• and
• receive the personalized output from the remote service and generate audio output indicating the personalized output.

A method, comprising:

• performing, by a voice-controlled device implemented by one or more computers:
• receiving audio input from a group of users over a time period, wherein the audio input includes voice commands associated with different items over the time period;
• generating, based on the audio input and via an encoder model trained using one or more machine learning techniques, a fixed-size representation of the group of users, wherein the fixed-size representation indicates a member composition of the group based on user classes detected in the group;
• uploading the fixed-size representation to a remote service that implements a machine learning model, wherein the machine learning model uses the fixed-size representation to generate a personalized output for the group of users;
• and
• receiving the personalized output from the remote service and generating audio output indicating the personalized output.

One or more non-transitory computer-accessible storage media storing program instructions that when executed on one or more processors of a voice-controlled device cause the voice-controlled device to:

• receive audio input from a group of users over a time period, wherein the audio input includes voice commands associated with different items over the time period;
• generate, based on the audio input and via an encoder model trained using one or more machine learning techniques, a fixed-size representation of the group of users, wherein the fixed-size representation indicates a member composition of the group based on user classes detected in the group;
• upload the fixed-size representation to a remote service that implements a machine learning model, wherein the machine learning model uses the fixed-size representation to generate a personalized output for the group of users;
• and
• receive the personalized output from the remote service and generate audio output indicating the personalized output.