MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation
This work addresses the need for more precise and personalized recommendations in explainable AI, though it is incremental as it builds on existing review-generation methods.
The paper tackles the problem of generating personalized and informative explanations in explainable recommendation by proposing MAPLE, a model that integrates aspect categories to improve review generation, and demonstrates significant performance improvements over baselines on two restaurant review datasets.
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.