A Review-Driven Neural Model for Sequential Recommendation
This work addresses the challenge of improving recommendation accuracy in E-commerce by leveraging review data, representing an incremental advancement in neural sequential recommendation methods.
The paper tackles the problem of sequential recommendation by incorporating semantic signals from user reviews to model both long-term preferences and short-term sequential patterns, achieving significant performance improvements over state-of-the-art models on three real-world datasets.
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.