Recently, deep learning methods have been shown to improve the performance of
recommender systems over traditional methods, especially when review text is
available. For example, a recent model, DeepCoNN, uses neural nets to learn one
latent representation for the text of all reviews written by a target user, and
a second latent representation for the text of all reviews for a target item,
and then combines these latent representations to obtain state-of-the-art
performance on recommendation tasks. We show that (unsurprisingly) much of the
predictive value of review text comes from reviews of the target user for the
target item. We then introduce a way in which this information can be used in
recommendation, even when the target user's review for the target item is not
available. Our model, called TransNets, extends the DeepCoNN model by
introducing an additional latent layer representing the target user-target item
pair. We then regularize this layer, at training time, to be similar to another
latent representation of the target user's review of the target item. We show
that TransNets and extensions of it improve substantially over the previous
state-of-the-art.Comment: Accepted for publication in the 11th ACM Conference on Recommender
Systems (RecSys 2017