Explanations in recommender systems help users better understand why recommendations are generated, which is crucial for enhancing users' trust and satisfaction. As recommender systems become ever more inscrutable, directly explaining recommender systems sometimes becomes impossible. Post-hoc explanation methods that do not elucidate internal mechanisms of recommender systems are popular approaches. State-of-art post-hoc explanation methods such as SHAP can generate explanations by building simpler surrogate models to approximate the original models. However, directly applying such methods has several concerns: (1) Posthoc explanations may not be faithful to the original recommender systems since the internal mechanisms of recommender systems are not elucidated; (2) The outputs returned by methods such as SHAP are not trivial for plain users to understand since background mathematical knowledge is required. In this work, we present an explanation method enhanced by SHAP that can generate easily understandable counterfactual explanations with high fidelity.