Real-time on the fly generation of feature-based label embeddings via machine learning

A computer-implemented method to train a machine-learned label embedding model in a label selection system, the method comprising:

• obtaining, by one or more computing devices, a plurality of training examples, the plurality of training examples comprising a positive label example associated with an entity and a negative label example unassociated with the entity;
• respectively inputting, by the one or more computing devices, each of the plurality of training examples into the machine-learned label embedding model that is configured to process the plurality of training examples to respectively generate a plurality of label embeddings;
• receiving, by the one or more computing devices, the plurality of label embeddings as an output of the machine-learned label embedding model;
• generating, by the one or more computing devices, a plurality of label scores by respectively comparing the plurality of label embeddings with an entity embedding associated with the entity, the plurality of label scores comprising a positive score for the positive label example and a negative score for the negative label example;
• and
• modifying, by the one or more computing devices, one or more parameters of the machine-learned label embedding model based at least in part on a loss function that provides a loss value based at least in part on the plurality of label scores, wherein the loss value is positively correlated with the negative score and negatively correlated with the positive score.

One or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations, the operations comprising;

• receiving an entity request that identifies an entity;
• accessing a machine-learned label embedding model that is personalized for and associated with the entity;
• identifying a plurality of potential labels for the entity;
• accessing a plurality of sets of label feature data respectively associated with the plurality of potential labels;
• respectively inputting each of the plurality of sets of label feature data into the machine-learned label embedding model that is configured to process the sets of label feature data to respectively generate a plurality of label embeddings;
• receiving the plurality of label embeddings as an output of the machine-learned label embedding model;
• generating a plurality of label scores by respectively comparing the plurality of label embeddings with an entity embedding associated with the entity;
• and
• selecting one or more of the potential labels to recommend to the entity based at least in part on the plurality of label scores.