Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for ranking items for presentation to a user based on a model that estimates value to the user. One method includes providing value-based training data, the training data including user features, features of corresponding content items presented to users, and respective values of a value variable determined from user behaviors with respect to content items presented to the users; training a scoring model on the training data to generate value-based scores from user content item features; ranking a plurality of candidate content items selected for a first user by a ranking engine, wherein the ranking engine receives respective value-based scores generated by the trained scoring model for the candidate content items and the first user; and providing two or more of the candidate content items for presentation to the first user in an order determined by the ranking.

Abstract: Methods and systems for generating entity embeddings for use with one or more machine learning models are described. The system comprises at least one storage device configured to implement a feature registry for storing features associated with at least one entity and at least one computer processor. The at least one computer processor is programmed to generate at least one entity embedding for the at least one entity, perform a plurality of benchmarking tasks on the generated at least one entity embedding to generate benchmarking data, and publish the at least one entity embedding and the benchmarking data to the feature registry to enable the at least one entity embedding to be shared among a plurality of machine learning models.