A Reusable Model-agnostic Framework for Faithfully Explainable Recommendation and System Scrutability
A Reusable Model-agnostic Framework for Faithfully Explainable Recommendation and System Scrutability
复制标题
DOI：
10.1145/3605357
复制
发表时间：
2023-06
影响因子：
5.6
通讯作者：
Zhichao Xu;Hansi Zeng;Juntao Tan;Zuohui Fu;Yongfeng Zhang;Qingyao Ai
中科院分区：
文献类型：
--
作者：
Zhichao Xu;Hansi Zeng;Juntao Tan;Zuohui Fu;Yongfeng Zhang;Qingyao Ai
最先进的工业级推荐系统应用大多采用复杂的模型结构，如深度神经网络。虽然这有助于提高模型性能，但这些近乎黑盒的模型导致的系统可解释性的缺乏也引起了人们的担忧，并可能削弱用户对系统的信任。现有的可解释推荐研究主要集中在设计可解释的模型结构，以生成模型内在的解释。然而，它们中的大多数具有复杂的结构，并且由于有效性和效率的考虑，难以将这些设计直接应用于现有的推荐应用。然而，虽然已经有一些关于在不知道其内部结构的情况下解释推荐模型的研究（即，模型不可知的解释），这些方法由于没有反映推荐模型的实际推理过程，或者换句话说，没有忠实性而受到批评。如何开发模型不可知论的解释方法，并根据忠实性来评估它们，大多是未知的。在这项工作中，我们提出了一个可重用的评估管道模型无关的可解释的建议。我们的管道从忠实性和可理解性的角度评估模型不可知论解释的质量。我们进一步提出了一个模型无关的解释框架的建议，并验证它与建议的评估管道。在公共数据集上的大量实验表明，我们的模型无关框架能够生成忠实于推荐模型的解释。此外，我们提供了定量和定性的研究表明，我们的解释框架可以提高黑箱推荐模型的可理解性。通过适当的修改，我们的评估管道和模型无关的解释框架可以很容易地迁移到现有的应用程序。通过这项工作，我们希望鼓励社区更多地关注可解释推荐系统的忠诚度评估。
State-of-the-art industrial-level recommender system applications mostly adopt complicated model structures such as deep neural networks. While this helps with the model performance, the lack of system explainability caused by these nearly blackbox models also raises concerns and potentially weakens the users’ trust in the system. Existing work on explainable recommendation mostly focuses on designing interpretable model structures to generate model-intrinsic explanations. However, most of them have complex structures, and it is difficult to directly apply these designs onto existing recommendation applications due to the effectiveness and efficiency concerns. However, while there have been some studies on explaining recommendation models without knowing their internal structures (i.e., model-agnostic explanations), these methods have been criticized for not reflecting the actual reasoning process of the recommendation model or, in other words, faithfulness. How to develop model-agnostic explanation methods and evaluate them in terms of faithfulness is mostly unknown. In this work, we propose a reusable evaluation pipeline for model-agnostic explainable recommendation. Our pipeline evaluates the quality of model-agnostic explanation from the perspectives of faithfulness and scrutability. We further propose a model-agnostic explanation framework for recommendation and verify it with the proposed evaluation pipeline. Extensive experiments on public datasets demonstrate that our model-agnostic framework is able to generate explanations that are faithful to the recommendation model. We additionally provide quantitative and qualitative study to show that our explanation framework could enhance the scrutability of blackbox recommendation model. With proper modification, our evaluation pipeline and model-agnostic explanation framework could be easily migrated to existing applications. Through this work, we hope to encourage the community to focus more on faithfulness evaluation of explainable recommender systems.