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

A Review-Driven Neural Model for Sequential Recommendation