# Hierarchical Aspect-guided Explanation Generation for Explainable Recommendation

arXiv: https://arxiv.org/abs/2110.10358 · HTML: https://mlsanity.com/paper/2110.10358/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 41 references that are in the map.
- First posted on arXiv 20 Oct 2021.

## Builds on (most cited first, 20 of 20)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [Graph U-Nets](https://mlsanity.com/paper/1905.05178/) (2019)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [Snapshot Ensembles: Train 1, get M for free](https://mlsanity.com/paper/1704.00109/) (2017)
- [Hierarchical Question-Image Co-Attention for Visual Question Answering](https://mlsanity.com/paper/1606.00061/) (2016)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [Deep Learning based Recommender System: A Survey and New Perspectives](https://mlsanity.com/paper/1707.07435/) (2017)
- [Neural Rating Regression with Abstractive Tips Generation for Recommendation](https://mlsanity.com/paper/1708.00154/) (2017)
- [Explainable Recommendation via Multi-Task Learning in Opinionated Text Data](https://mlsanity.com/paper/1806.03568/) (2018)
- [Generate Natural Language Explanations for Recommendation](https://mlsanity.com/paper/2101.03392/) (2021)
- [Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews](https://mlsanity.com/paper/1802.07938/) (2018)
- [Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network](https://mlsanity.com/paper/2010.01480/) (2020)
- [TransNets: Learning to Transform for Recommendation](https://mlsanity.com/paper/1704.02298/) (2017)
- [Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations](https://mlsanity.com/paper/1901.06965/) (2019)
- [Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decoding](https://mlsanity.com/paper/1906.05667/) (2019)
- [A Context-Aware User-Item Representation Learning for Item Recommendation](https://mlsanity.com/paper/1712.02342/) (2017)
- [Context-aware Natural Language Generation with Recurrent Neural Networks](https://mlsanity.com/paper/1611.09900/) (2016)

## Abstract (arXiv)

Explainable recommendation systems provide explanations for recommendation results to improve their transparency and persuasiveness. The existing explainable recommendation methods generate textual explanations without explicitly considering the user's preferences on different aspects of the item. In this paper, we propose a novel explanation generation framework, named Hierarchical Aspect-guided explanation Generation (HAG), for explainable recommendation. Specifically, HAG employs a review-based syntax graph to provide a unified view of the user/item details. An aspect-guided graph pooling operator is proposed to extract the aspect-relevant information from the review-based syntax graphs to model the user's preferences on an item at the aspect level. Then, a hierarchical explanation decoder is developed to generate aspects and aspect-relevant explanations based on the attention mechanism. The experimental results on three real datasets indicate that HAG outperforms state-of-the-art explanation generation methods in both single-aspect and multi-aspect explanation generation tasks, and also achieves comparable or even better preference prediction accuracy than strong baseline methods.