Abstract: Methods, systems, and computer-readable storage media for receiving a set of descriptions provided as unstructured data, each description associated with one or more entities in a set of entities that can be queried using the IR system, providing, from the set of descriptions, a first set of training data including at least a first set of entities including at least a portion of the set of entities, training a named-entity recognition (NER) model using at least a portion of the first set of training data, receiving, by the IR system, a portion of a search query, and providing a set of auto-complete suggestions based on the portion of the search query and the NER model.

Abstract: Methods, systems, and media for providing a user vector including a plurality of user attributes, each user attribute having a value assigned thereto, the user vector being representative of a user, determining a user latent vector by processing the user vector through an attribute embedding look-up, and an attention layer, and for each item in a set of items: providing an item vector including a plurality of item attributes, each item attribute having a value assigned thereto, the item vector being specific to an item in the set of items, determining an item latent vector by processing the item vector through the attribute embedding look-up, and the attention layer, and processing the user and item latent vectors through connected layers to extract higher order features, and learn relationships between the user, and the item, and to provide a user-item score that represents a compatibility between the user and the item.