SIMULTANEOUS LEARNING OF INPUTS AND PARAMETERS IN MACHINE LEARNING-BASED RECOMMENDER SYSTEMS

The present disclosure relates to a recommender system and method. Unlike known systems, which learn neural network parameters during training and fix the input vectors, the recommender system learns both the input vectors and machine learning model parameters during training. In one embodiment, the initial user and item input vectors are interaction vectors that are based on known and unknown user feedback. The non-zero elements of the interaction vectors correspond user-item pairs for which feedback is known, and the zero elements corresponding to user-item pairs for which feedback is unknown. The non-zero elements of the interaction vectors are learnable parameters during the training phase. The user and item vectors, as well as the model parameters, learned during the training phase are used in a prediction and recommendation phase to make product recommendations for a user.