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.

本文中，我们开发了一种新颖的神经网络模型，通过非对称注意模块恰当地解决了用户评价和产品评价的不同。多层次范式考虑到了不同的评价和句子的重要性和相关性。在各种真实数据集上的广泛实验结果表明了我们方法的有效性。