Reinforced model-agnostic counterfactual explanations for recommender systems
Ao Chang, Qingxian Wang · 2023
Explanation is an important requirement for transparent and trustworthy recommender systems. When the recommendation model itself is not explainable, an explanation must be generated post-hoc. In contrast to traditional post-hoc explanation methods, counterfactual methods can provide scrutable and actionable explanations with high fidelity. Existing counterfactual explanation methods for recommender systems are either not generalizable or face a huge search space. In this work, we propose a reinforcement learning counterfactual explanation method MACER (Model-Agnostic Counterfactual Explanations for Recommender Systems) which generates item-based explanations for recommender systems. We embed the discrete action space into a continuous space, making it possible to use the process of finding counterfactual explanations as a task of reinforcement learning. This method treats the recommender system as a black box (model-agnostic) and has no requirement on the type of recommender system, and thus is applicable to all recommendation systems.