Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network

TL;DR

This paper introduces a novel knowledge-enhanced personalized review generation model using capsule graph neural networks, effectively integrating item attributes from a knowledge graph to produce more informative and user-preference aligned reviews.

Contribution

The paper proposes the first use of knowledge graphs in personalized review generation, leveraging capsule GNNs for better aspect and content modeling.

Findings

Outperforms existing models on three real-world datasets.

Effectively encodes product attributes for more informative reviews.

Improves alignment with user preferences at multiple levels.

Abstract

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.