Generating machine-learned entity embeddings based on online interactions and semantic context

Abstract

Techniques for extracting features of entities and targets that can be applied in a set of applications, such as entity selection prediction, audience expansion, feed relevance, and job recommendation. In one technique, entity interaction data is stored that indicates, for each of multiple entities, one or more targets that are associated with items with which the entity interacted. Token association data is stored that indicates, for each of multiple tokens, one or more targets that are associated with the token. Then, using one or more machine learning techniques, entity embeddings and target embeddings are generated based on the entity interaction data and the token association data. Later, a request for content is received from a particular entity. Based on at least one entity embedding, a content item for the particular entity is identified. The content item is transferred over a computer network and presented to the particular entity.