SYSTEM AND METHOD FOR DEEP LEARNING RECOMMENDER

摘要

Recommendations are generated for users by identifying items held by the users defined by a shallow representation and attributes; defining the items based on a deep representation derived from attributes; generating a deep holding matrix identifying the items held by the users based on deep representations; generating a shallow holding matrix identifying the items held by the users based on shallow representations; generating a similarity score matrix between the deep representations; decomposing the shallow holding matrix into a user latent representation and a product feature latent representation; concatenating the product feature latent representation and product information and pass to a first neural network; concatenating the user latent representation and user information and pass to a second neural network; performing a dot product matrix multiplication on the output of the first neural and the output of the second neural network to generate, for every user and every product, a probability.