SYSTEM, METHOD, AND COMPUTER PROGRAM FOR RECOMMENDING ITEMS USING A DIRECT NEURAL NETWORK STRUCTURE

The present disclosure relates to a system, method, and computer program for recommending products using a neural network architecture that directly learns a user's predicted rating for an item from user and item data. A set of encoding neural networks maps each input source for user and item data to a lower-dimensional vector space. The individual lower-dimensional vector outputs of the encoding neural networks are combined to create a single multidimensional vector representation of user and item data. A prediction neural network is trained to predict a user's rating for an item based on the single multidimensional vector representation of user and item data. The neural network architecture allows for more efficient optimization and faster convergence that recommendations systems that rely on autoencoders. The system recommends items to users based on the users' predicted ratings for items.