# A Review-Driven Neural Model for Sequential Recommendation

arXiv: https://arxiv.org/abs/1907.00590 · HTML: https://mlsanity.com/paper/1907.00590/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Filter-enhanced MLP is All You Need for Sequential Recommendation](https://mlsanity.com/paper/2202.13556/) (2022)
- [FineRec:Exploring Fine-grained Sequential Recommendation](https://mlsanity.com/paper/2404.12975/) (2024)
- [TriMLP: Revenge of a MLP-like Architecture in Sequential Recommendation](https://mlsanity.com/paper/2305.14675/) (2023)
- [Graphing else matters: exploiting aspect opinions and ratings in explainable graph-based recommendations](https://mlsanity.com/paper/2107.03226/) (2021)
- [Modeling Complex Dependencies for Session-based Recommendations via Graph Neural Networks](https://mlsanity.com/paper/2201.12532/) (2022)

## Abstract (arXiv)

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.