Explainable Recommendation has been gaining attention over the last few years in industry and academia. Explanations provided along with recommendations in a recommender system framework have many uses: particularly reasoning why a suggestion is provided and how well an item aligns with a user's personalized preferences. Hence, explanations can play a huge role in influencing users to purchase products. However, the reliability of the explanations under varying scenarios has not been strictly verified from an empirical perspective. Unreliable explanations can bear strong consequences such as attackers leveraging explanations for manipulating and tempting users to purchase target items that the attackers would want to promote. In this paper, we study the vulnerability of existent feature-oriented explainable recommenders, particularly analyzing their performance under different levels of external noises added into model parameters. We conducted experiments by analyzing three important state-of-the-art (SOTA) explainable recommenders when trained on two widely used e-commerce based recommendation datasets of different scales. We observe that all the explainable models are vulnerable to increased noise levels. Experimental results verify our hypothesis that the ability to explain recommendations does decrease along with increasing noise levels and particularly adversarial noise does contribute to a much stronger decrease. Our study presents an empirical verification on the topic of robust explanations in recommender systems which can be extended to different types of explainable recommenders in RS.

翻译：近年来，可解释推荐在工业界和学术界受到广泛关注。在推荐系统框架中，与推荐结果一同提供的解释具有多种用途，特别是用于说明推荐理由以及项目与用户个性化偏好的匹配程度。因此，解释在影响用户购买决策方面发挥着重要作用。然而，从实证角度而言，解释在不同场景下的可靠性尚未得到严格验证。不可靠的解释可能带来严重后果，例如攻击者可利用解释来操纵和诱使用户购买其希望推广的目标项目。本文研究了现有面向特征的可解释推荐系统的脆弱性，重点分析了在不同水平的外部噪声添加到模型参数时系统的表现。我们通过实验分析了三种重要的最先进可解释推荐模型，并在两个不同规模的电商推荐基准数据集上进行训练。观察发现，所有可解释模型均易受噪声水平升高的影响。实验结果验证了我们的假设：推荐解释能力随噪声水平升高而下降，尤其对抗性噪声会导致更显著的性能衰减。本研究对推荐系统中鲁棒解释问题提供了实证验证，该结论可推广至推荐系统中不同类型的可解释推荐模型。