  Recently, recommender systems have been able to emit substantially improved
recommendations by leveraging user-provided reviews. Existing methods typically
merge all reviews of a given user or item into a long document, and then
process user and item documents in the same manner. In practice, however, these
two sets of reviews are notably different: users' reviews reflect a variety of
items that they have bought and are hence very heterogeneous in their topics,
while an item's reviews pertain only to that single item and are thus topically
homogeneous. In this work, we develop a novel neural network model that
properly accounts for this important difference by means of asymmetric
attentive modules. The user module learns to attend to only those signals that
are relevant with respect to the target item, whereas the item module learns to
extract the most salient contents with regard to properties of the item. Our
multi-hierarchical paradigm accounts for the fact that neither are all reviews
equally useful, nor are all sentences within each review equally pertinent.
Extensive experimental results on a variety of real datasets demonstrate the
effectiveness of our method.
