  A recommender system's basic task is to estimate how users will respond to
unseen items. This is typically modeled in terms of how a user might rate a
product, but here we aim to extend such approaches to model how a user would
write about the product. To do so, we design a character-level Recurrent Neural
Network (RNN) that generates personalized product reviews. The network
convincingly learns styles and opinions of nearly 1000 distinct authors, using
a large corpus of reviews from BeerAdvocate.com. It also tailors reviews to
describe specific items, categories, and star ratings. Using a simple input
replication strategy, the Generative Concatenative Network (GCN) preserves the
signal of static auxiliary inputs across wide sequence intervals. Without any
additional training, the generative model can classify reviews, identifying the
author of the review, the product category, and the sentiment (rating), with
remarkable accuracy. Our evaluation shows the GCN captures complex dynamics in
text, such as the effect of negation, misspellings, slang, and large
vocabularies gracefully absent any machinery explicitly dedicated to the
purpose.
