Human conversations of recommendation naturally involve the shift of
interests which can align the recommendation actions and conversation process
to make accurate recommendations with rich explanations. However, existing
conversational recommendation systems (CRS) ignore the advantage of user
interest shift in connecting recommendation and conversation, which leads to an
ineffective loose coupling structure of CRS. To address this issue, by modeling
the recommendation actions as recommendation paths in a knowledge graph (KG),
we propose DICR (Dual Imitation for Conversational Recommendation), which
designs a dual imitation to explicitly align the recommendation paths and user
interest shift paths in a recommendation module and a conversation module,
respectively. By exchanging alignment signals, DICR achieves bidirectional
promotion between recommendation and conversation modules and generates
high-quality responses with accurate recommendations and coherent explanations.
Experiments demonstrate that DICR outperforms the state-of-the-art models on
recommendation and conversation performance with automatic, human, and novel
explainability metrics.

本文提出 DICR，通过将推荐动作建模为知识图谱中的推荐路径，设计了双向模仿来明确地对齐推荐模块和对话模块中的推荐路径和用户兴趣变化路径，从而实现了推荐和对话模块之间的双向促进，并生成具有准确推荐和连贯解释的高质量响应。实验结果表明 DICR 在推荐和对话性能以及自动化、人类和新颖可解释性指标方面优于现有技术水平。

通过双重模仿实现推荐与对话的对齐

Aligning Recommendation and Conversation via Dual Imitation

Following a fast initial breakthrough in graph based learning, Graph Neural
Networks (GNNs) have reached a widespread application in many science and
engineering fields, prompting the need for methods to understand their decision
process.
GNN explainers have started to emerge in recent years, with a multitude of
methods both novel or adapted from other domains. To sort out this plethora of
alternative approaches, several studies have benchmarked the performance of
different explainers in terms of various explainability metrics. However, these
earlier works make no attempts at providing insights into why different GNN
architectures are more or less explainable, or which explainer should be
preferred in a given setting.
In this survey, we fill these gaps by devising a systematic experimental
study, which tests ten explainers on eight representative architectures trained
on six carefully designed graph and node classification datasets. With our
results we provide key insights on the choice and applicability of GNN
explainers, we isolate key components that make them usable and successful and
provide recommendations on how to avoid common interpretation pitfalls. We
conclude by highlighting open questions and directions of possible future
research.

本文通过实验研究，测试了十种解释器在八种代表性架构训练的六个仔细设计的图形和节点分类数据集上的表现，提供了 GNN 解释器选择和适用性的关键见解，分离了使它们可用的关键组件并提供了关于如何避免常见解释误区的建议，并强调了未来研究的开放性问题和方向。

在图神经网络中解释解释者：一项比较研究

Explaining the Explainers in Graph Neural Networks: a Comparative Study

Explaining the generalization characteristics of deep learning is an emerging
topic in advanced machine learning. There are several unanswered questions
about how learning under stochastic optimization really works and why certain
strategies are better than others. In this paper, we address the following
question: \textit{can we probe intermediate layers of a deep neural network to
identify and quantify the learning quality of each layer?} With this question
in mind, we propose new explainability metrics that measure the redundant
information in a network's layers using a low-rank factorization framework and
quantify a complexity measure that is highly correlated with the generalization
performance of a given optimizer, network, and dataset. We subsequently exploit
these metrics to augment the Stochastic Gradient Descent (SGD) optimizer by
adaptively adjusting the learning rate in each layer to improve in
generalization performance. Our augmented SGD -- dubbed RMSGD -- introduces
minimal computational overhead compared to SOTA methods and outperforms them by
exhibiting strong generalization characteristics across application,
architecture, and dataset.

通过提出一种新的解释性指标来量化神经网络中冗余信息的质量， 并量化与优化器、网络和数据集的泛化性能高度相关的复杂度措施来增强随机梯度下降（SGD）的方法，我们提出了一种可以对深度神经网络中间层进行探索并给出量化学习质量的方法，并将其应用于 RMSGD，在应用、架构和数据集上表现出良好的泛化特性。