  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.
