1. A system comprising: a computer-readable memory storing executable instructions; and one or more physical computer processors in communication with the computer-readable memory, wherein the one or more physical computer processors are programmed by the executable instructions to at least: obtain a plurality of sequences of movie identifiers indicating a history of movies watched by a plurality of users; identify a first history randomization profile for the plurality of sequences of movie identifiers, the first history randomization profile including information indicating: every movie identified in each of the plurality of sequences of movie identifiers is a candidate for randomization, wherein a candidate for randomization is an item included in a sequence of movie identifiers that may be moved to a new location within the sequence of movie identifiers; or a first subset of movies identified in each of the plurality of sequences of movie identifiers which are candidates for randomization; generate a plurality of randomized sequences of movie identifiers by randomizing the plurality of sequences of movie identifiers using the first history randomization profile; transmit the plurality of randomized sequences of movie identifiers to a recurrent neural network language modeling device; receive a recurrent neural network language model including latent feature vectors identifying a categorization for an input sequence of previously accessed movies, the categorization describing the sequence of previously accessed movies; receive a request from an access device of a user of the plurality of users, the request including information indicative of an access history for the user; and identify a user sequence categorization for the user using the recurrent neural network language model and the access history for the user.