Abstract: An example may, at a first device, input an entity embedding for an entity and an item embedding for a plurality of items to a scoring function. The entity embedding and the item embedding are pre-computed using a first language model. At the first device, a retrieval score for the entity and a first item of the plurality of items is computed. The retrieval score is used to identify an item subset of the plurality of items. A ranking prompt is input to a second language model. The second language model and the first language model have a common parameter value. The second language model generates a ranking score for the entity and a second item in response to the ranking prompt. An online system uses the ranking score to include or exclude the second item from a presentation of digital content to the entity via a device.

Abstract: An example may train a cross encoder embedding model using a ranking instruction, a combined input, a pseudo label, and a combined loss. The combined loss includes a ranking loss and a first retrieval loss. A first entity embedding of an entity and a first item embedding of an item may be obtained from the trained cross encoder embedding model. A first input including the first entity embedding obtained from the trained cross encoder embedding model, a second input including the first item embedding obtained from the trained cross encoder embedding model, and a second retrieval loss, may be used to train a dual encoder retrieval model to produce a trained dual encoder retrieval model. A system may use output of the trained dual encoder retrieval model to include or exclude items from a presentation of digital content items to the entity via a device.

Abstract: An example formulates a training input for a neural network model with attention to include action data and descriptive content. The action data includes a first entity identifier (ID) and a first sequence of actions associated with the first entity ID. The descriptive content describes a first entity associated with the first entity ID. An action in the first sequence of actions includes an electronic transmission involving the first entity and a second entity. An example uses the training input, including the first entity ID, and a non-standardized tokenizer, to train the neural network model with attention to generate and output a second sequence of actions.