Abstract
Writing review for a purchased item is a unique
channel to express a user’s opinion in ECommerce. 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 unionlevel 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