MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation

TL;DR

MAPLE is a novel model that improves review generation for explainable recommendations by incorporating multi-aspect prompts, leading to more personalized, diverse, and accurate explanations.

Contribution

Introduces MAPLE, a multi-aspect prompt learning model that enhances review generation with fine-grained aspect control for better explainability.

Findings

Outperforms baseline models in review quality and diversity

Generates more personalized and aspect-aware explanations

Maintains high coherence and factual relevance

Abstract

The Explainable Recommendation task is designed to receive a pair of user and item and output explanations to justify why an item is recommended to a user. Many models approach review generation as a proxy for explainable recommendations. While these models can produce fluent and grammatically correct sentences, they often lack precision and fail to provide personalized, informative recommendations. To address this issue, we propose a personalized, aspect-controlled model called Multi-Aspect Prompt LEarner (MAPLE), which integrates aspect category as another input dimension to facilitate memorizing fine-grained aspect terms. Experiments conducted on two real-world review datasets in the restaurant domain demonstrate that MAPLE significantly outperforms baseline review-generation models. MAPLE excels in both text and feature diversity, ensuring that the generated content covers a wide range of aspects. Additionally, MAPLE delivers good generation quality while maintaining strong coherence and factual relevance. The code and dataset used in this paper can be found here https://github.com/Nana2929/MAPLE.git.