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.

通过使用知识图谱方法和协同过滤技术，我们的研究提出了一种可以生成客观描述的推荐解释的方法，该方法结合了用户购买历史和物品特征，为用户提供与其偏好相匹配的个性化解释，实验结果表明该方法在自然语言可解释推荐方面优于现有的最新模型。