Hierarchical Aspect-guided Explanation Generation for Explainable Recommendation

TL;DR

This paper introduces HAG, a hierarchical explanation generation framework for recommender systems that explicitly models user preferences on different aspects using review-based syntax graphs, improving explanation quality and prediction accuracy.

Contribution

HAG is a novel framework that combines syntax graphs and aspect-guided pooling with hierarchical decoding for better explainable recommendations.

Findings

HAG outperforms existing explanation methods in multiple datasets.

HAG achieves comparable or better recommendation accuracy.

HAG effectively models user preferences at the aspect level.

Abstract

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.