A Review-Driven Neural Model for Sequential Recommendation

A Review-Driven Neural Model for Sequential Recommendation

Chenliang Li, Xichuan Niu, Xiangyang Luo, Zhenzhong Chen, Cong Quan

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 2866-2872. https://doi.org/10.24963/ijcai.2019/397

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 user's 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.
Keywords:
Machine Learning: Recommender Systems
Multidisciplinary Topics and Applications: Information Retrieval
Multidisciplinary Topics and Applications: Recommender Systems