ATTENTIVE NEURAL COLLABORATIVE FILTERING FOR MODELING IMPLICIT FEEDBACK

摘要

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.