  Writing review for a purchased item is a unique channel to express a user's
opinion in E-Commerce. Recently, many deep learning based solutions have been
proposed by exploiting user reviews for rating prediction. In contrast, there
has been few attempt to enlist the semantic signals covered by user reviews for
the task of collaborative filtering. In this paper, we propose a novel
review-driven neural sequential recommendation model (named RNS) by considering
users' intrinsic preference (long-term) and sequential patterns (short-term).
In detail, RNS is devised to encode each user or item with the aspect-aware
representations extracted from the reviews. Given a sequence of historical
purchased items for a user, we devise a novel hierarchical attention over
attention mechanism to capture sequential patterns at both union-level and
individual-level. Extensive experiments on three real-world datasets of
different domains demonstrate that RNS obtains significant performance
improvement over uptodate state-of-the-art sequential recommendation models.
