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; transmit the plurality of sequences of movie identifiers to a recurrent neural network language modeling device; receive a recurrent neural network language model including latent feature vectors identifying, for a first movie, a degree of relatedness between the first movie and one or more tags; obtain a training data set, the training data set including tagged movies wherein a tagged movie comprises a movie identifier and a tag; generate a tagging suggestion model using the training data set, wherein inputs to the tagging suggestion model include latent features included in the latent feature vectors for a tagged movie and wherein the tagging suggestion model outputs one or more suggested tag; obtain a suggested tag and a confidence score for an untagged movie using the tagging suggestion model; generate a tagging priority for the untagged movie using: the confidence score for the suggested tag; and a number of times the untagged movie is accessed or a number of times movies tagged with the suggested tag are accessed; and selectively route the untagged movie using the suggested tag and the tagging priority for the untagged movie to one of: a manual tagging system for manual assignment of a tag for the untagged movie; a tagging quality control system for approval of the suggested tag for the untagged movie; or an automated tagging system for persisting the suggested tag for the untagged movie.