Explanations accompanied by a recommendation can assist users in understanding the decision made by recommendation systems, which in turn increases a user's confidence and trust in the system. Recently, research has focused on generating natural language explanations in a human-readable format. Thus far, the proposed approaches leverage item reviews written by users, which are often subjective, sparse in language, and unable to account for new items that have not been purchased or reviewed before. Instead, we aim to generate fact-grounded recommendation explanations that are objectively described with item features while implicitly considering a user's preferences, based on the user's purchase history. To achieve this, we propose a knowledge graph (KG) approach to natural language explainable recommendation. Our approach draws on user-item features through a novel collaborative filtering-based KG representation to produce fact-grounded, personalized explanations, while jointly learning user-item representations for recommendation scoring. Experimental results show that our approach consistently outperforms previous state-of-the-art models on natural language explainable recommendation.

翻译：伴随推荐生成的解释能够帮助用户理解推荐系统的决策，从而提升用户对系统的信心与信任度。近期研究聚焦于以人类可读形式生成自然语言解释。然而，现有方法多依赖用户撰写的商品评论，此类评论常带有主观性、语言稀疏，且无法覆盖尚未购买或未评论的新商品。为此，我们旨在生成基于事实的推荐解释——即通过隐式考虑用户偏好（基于其购买历史），以商品特征进行客观描述。为实现这一目标，我们提出一种基于知识图谱（KG）的方法用于自然语言可解释推荐。该方法通过新颖的协同过滤知识图谱表示抽取用户-商品特征，从而生成基于事实的个性化解释，同时联合学习用于推荐评分的用户-商品表示。实验结果表明，我们的方法在自然语言可解释推荐任务中持续优于现有最先进模型。