MULTI-OUTPUT HEADED ENSEMBLES FOR PRODUCT CLASSIFICATION

An item classification method and system using multi-output headed ensembles, that can include receiving one or more text input sequences at one or more first estimator threads corresponding to the one or more text input sequences. The method can also include tokenizing the one or more text input sequences into one or more first tokens within the one or more first estimator threads. In addition, the method can include outputting one or more item classifications based on an output of the one or more first estimator threads. Further, the method may include applying a backpropagation algorithm to update network weights connecting neural layers in the first estimator threads, defining an optimal setting of network parameters using cross-validation with respect to the first estimator threads, and mapping the one or more first tokens to an embedding space within the one or more first estimator threads.