2019/07/01 by Chenliang Li, Li, Chenliang, Xichuan Niu +7 · 2 citations
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Recommender Systems and Techniques #Sentiment Analysis and Opinion Mining
paper · pdf · doi:10.48550/arxiv.1907.00590
openalex publication_date 2019/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.