# Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network

arXiv: https://arxiv.org/abs/2010.01480 · HTML: https://mlsanity.com/paper/2010.01480/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2020.

## Built on by (most cited first, 14 of 14)

- [Pretrained Language Models for Text Generation: A Survey](https://mlsanity.com/paper/2201.05273/) (2022)
- [LongLaMP: A Benchmark for Personalized Long-form Text Generation](https://mlsanity.com/paper/2407.11016/) (2024)
- [Pretrained Language Models for Text Generation: A Survey](https://mlsanity.com/paper/2105.10311/) (2021)
- [Teach LLMs to Personalize -- An Approach inspired by Writing Education](https://mlsanity.com/paper/2308.07968/) (2023)
- [Learning to Rewrite Prompts for Personalized Text Generation](https://mlsanity.com/paper/2310.00152/) (2023)
- [Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling](https://mlsanity.com/paper/2112.13556/) (2021)
- [Review-LLM: Harnessing Large Language Models for Personalized Review Generation](https://mlsanity.com/paper/2407.07487/) (2024)
- [Knowledge-based Review Generation by Coherence Enhanced Text Planning](https://mlsanity.com/paper/2105.03815/) (2021)
- [KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks](https://mlsanity.com/paper/2302.11396/) (2023)
- [UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation](https://mlsanity.com/paper/2209.13885/) (2022)
- [Interpreting User Requests in the Context of Natural Language Standing Instructions](https://mlsanity.com/paper/2311.09796/) (2023)
- [Online Personalizing White-box LLMs Generation with Neural Bandits](https://mlsanity.com/paper/2404.16115/) (2024)
- [TeKo: Text-Rich Graph Neural Networks with External Knowledge](https://mlsanity.com/paper/2206.07253/) (2022)
- [Hierarchical Aspect-guided Explanation Generation for Explainable Recommendation](https://mlsanity.com/paper/2110.10358/) (2021)

## Abstract (arXiv)

Personalized review generation (PRG) aims to automatically produce review text reflecting user preference, which is a challenging natural language generation task. Most of previous studies do not explicitly model factual description of products, tending to generate uninformative content. Moreover, they mainly focus on word-level generation, but cannot accurately reflect more abstractive user preference in multiple aspects. To address the above issues, we propose a novel knowledge-enhanced PRG model based on capsule graph neural network~(Caps-GNN). We first construct a heterogeneous knowledge graph (HKG) for utilizing rich item attributes. We adopt Caps-GNN to learn graph capsules for encoding underlying characteristics from the HKG. Our generation process contains two major steps, namely aspect sequence generation and sentence generation. First, based on graph capsules, we adaptively learn aspect capsules for inferring the aspect sequence. Then, conditioned on the inferred aspect label, we design a graph-based copy mechanism to generate sentences by incorporating related entities or words from HKG. To our knowledge, we are the first to utilize knowledge graph for the PRG task. The incorporated KG information is able to enhance user preference at both aspect and word levels. Extensive experiments on three real-world datasets have demonstrated the effectiveness of our model on the PRG task.