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。

基于知识的自然语言推荐解释

Knowledge-grounded Natural Language Recommendation Explanation

Automatic generation of medical reports from X-ray images can assist
radiologists to perform the time-consuming and yet important reporting task.
Yet, achieving clinically accurate generated reports remains challenging.
Modeling the underlying abnormalities using the knowledge graph approach has
been found promising in enhancing the clinical accuracy. In this paper, we
introduce a novel fined-grained knowledge graph structure called an attributed
abnormality graph (ATAG). The ATAG consists of interconnected abnormality nodes
and attribute nodes, allowing it to better capture the abnormality details. In
contrast to the existing methods where the abnormality graph was constructed
manually, we propose a methodology to automatically construct the fine-grained
graph structure based on annotations, medical reports in X-ray datasets, and
the RadLex radiology lexicon. We then learn the ATAG embedding using a deep
model with an encoder-decoder architecture for the report generation. In
particular, graph attention networks are explored to encode the relationships
among the abnormalities and their attributes. A gating mechanism is adopted and
integrated with various decoders for the generation. We carry out extensive
experiments based on the benchmark datasets, and show that the proposed
ATAG-based deep model outperforms the SOTA methods by a large margin and can
improve the clinical accuracy of the generated reports.

本文提出了一种基于属性异常图（ATAG）的深度模型，用于从医学图像自动生成医学报告，并在标准数据集上验证其诊断准确性表现优异。