25. The non-transitory computer readable medium of claim 16 , wherein generating the language model comprises generating a neural network prediction model, wherein generating the language model comprises: generating a latent feature language model using the randomized plurality of sequences of item identifiers as the words of the language modeled by the latent feature language model; obtaining a training data set, the training data set including sequences of item identifiers and previously assigned predictions for the sequences; obtaining latent features for the sequences of item identifiers; and generating the neural network prediction model using the training data set, wherein inputs to the neural network prediction model include the obtained latent features for the sequences of item identifiers.
16. A non-transitory computer readable medium storing a computer-executable module that, when executed by a processor of a computing device, cause the computing device to perform a process comprising: obtaining a plurality of sequences of item identifiers indicating a history of items accessed; randomizing an order of item identifiers included in at least a portion of the plurality of sequences of item identifiers to generate a randomized plurality of sequences of item identifiers; generating a language model using the randomized plurality of sequences of item identifiers, the language model configured to provide one or more candidate categories for items of a processing target; receiving a request from an access device of a processing target, the request including information indicative of items for the processing target; generating a candidate prediction for the processing target using the language model and the item for the processing target; and providing a prediction for the processing target using the candidate prediction.