Latent representations¶
Classes defining user and item latent representations in factorization models.

class
spotlight.factorization.representations.
BilinearNet
(num_users, num_items, embedding_dim=32, user_embedding_layer=None, item_embedding_layer=None, sparse=False)[source]¶ Bilinear factorization representation.
Encodes both users and items as an embedding layer; the score for a useritem pair is given by the dot product of the item and user latent vectors.
Parameters:  num_users (int) – Number of users in the model.
 num_items (int) – Number of items in the model.
 embedding_dim (int, optional) – Dimensionality of the latent representations.
 user_embedding_layer (an embedding layer, optional) – If supplied, will be used as the user embedding layer of the network.
 item_embedding_layer (an embedding layer, optional) – If supplied, will be used as the item embedding layer of the network.
 sparse (boolean, optional) – Use sparse gradients.