Denoising Diffusion Probabilistic Model (DDPM) has shown great competence in
image and audio generation tasks. However, there exist few attempts to employ
DDPM in the text generation, especially review generation under recommendation
systems. Fueled by the predicted reviews explainability that justifies
recommendations could assist users better understand the recommended items and
increase the transparency of recommendation system, we propose a Diffusion
Model-based Review Generation towards EXplainable Recommendation named
Diffusion-EXR. Diffusion-EXR corrupts the sequence of review embeddings by
incrementally introducing varied levels of Gaussian noise to the sequence of
word embeddings and learns to reconstruct the original word representations in
the reverse process. The nature of DDPM enables our lightweight Transformer
backbone to perform excellently in the recommendation review generation task.
Extensive experimental results have demonstrated that Diffusion-EXR can achieve
state-of-the-art review generation for recommendation on two publicly available
benchmark datasets.

基于扩散模型的评论生成为可解释的推荐提出了一种名为 Diffusion-EXR 的方法，并展示了其在两个公开基准数据集上能够达到最先进的评论生成效果。

Diffusion-EXR: 通过扩散模型实现可控的解释推荐的评论生成

Diffusion-EXR: Controllable Review Generation for Explainable  Recommendation via Diffusion Models

Reviews contain rich information about product characteristics and user
interests and thus are commonly used to boost recommender system performance.
Specifically, previous work show that jointly learning to perform review
generation improves rating prediction performance. Meanwhile, these
model-produced reviews serve as recommendation explanations, providing the user
with insights on predicted ratings. However, while existing models could
generate fluent, human-like reviews, it is unclear to what degree the reviews
fully uncover the rationale behind the jointly predicted rating. In this work,
we perform a series of evaluations that probes state-of-the-art models and
their review generation component. We show that the generated explanations are
brittle and need further evaluation before being taken as literal rationales
for the estimated ratings.

通过评估当前的推荐系统和其生成评论的组件，我们发现生成的评论是脆弱的且在被视作预测评级的文字理由之前有待进一步评估。

关于推荐系统语言解释的忠实度和连贯性问题

On Faithfulness and Coherence of Language Explanations for Recommendation Systems

As a natural language generation task, it is challenging to generate
informative and coherent review text. In order to enhance the informativeness
of the generated text, existing solutions typically learn to copy entities or
triples from knowledge graphs (KGs). However, they lack overall consideration
to select and arrange the incorporated knowledge, which tends to cause text
incoherence.
To address the above issue, we focus on improving entity-centric coherence of
the generated reviews by leveraging the semantic structure of KGs. In this
paper, we propose a novel Coherence Enhanced Text Planning model (CETP) based
on knowledge graphs (KGs) to improve both global and local coherence for review
generation. The proposed model learns a two-level text plan for generating a
document: (1) the document plan is modeled as a sequence of sentence plans in
order, and (2) the sentence plan is modeled as an entity-based subgraph from
KG. Local coherence can be naturally enforced by KG subgraphs through
intra-sentence correlations between entities. For global coherence, we design a
hierarchical self-attentive architecture with both subgraph- and node-level
attention to enhance the correlations between subgraphs. To our knowledge, we
are the first to utilize a KG-based text planning model to enhance text
coherence for review generation. Extensive experiments on three datasets
confirm the effectiveness of our model on improving the content coherence of
generated texts.

本文提出了一种基于知识图谱（KG）的一体化增强文本规划方法（CETP），利用 KG 的语义结构来提高生成评论的全局和局部连贯性。通过学习文档的两级文本计划，即将文档计划建模为按顺序排列的句子计划的序列以及将句子计划建模为 KG 中的基于实体的子图，进而自然地强化段落内部的实体之间的相关性和段落之间的相关性。通过在三个数据集上的广泛实验证明了我们的模型在提高生成文本内容连贯性方面的有效性。