Counterfactual explanations interpret the recommendation mechanism via
exploring how minimal alterations on items or users affect the recommendation
decisions. Existing counterfactual explainable approaches face huge search
space and their explanations are either action-based (e.g., user click) or
aspect-based (i.e., item description). We believe item attribute-based
explanations are more intuitive and persuadable for users since they explain by
fine-grained item demographic features (e.g., brand). Moreover, counterfactual
explanation could enhance recommendations by filtering out negative items.
In this work, we propose a novel Counterfactual Explainable Recommendation
(CERec) to generate item attribute-based counterfactual explanations meanwhile
to boost recommendation performance. Our CERec optimizes an explanation policy
upon uniformly searching candidate counterfactuals within a reinforcement
learning environment. We reduce the huge search space with an adaptive path
sampler by using rich context information of a given knowledge graph. We also
deploy the explanation policy to a recommendation model to enhance the
recommendation. Extensive explainability and recommendation evaluations
demonstrate CERec's ability to provide explanations consistent with user
preferences and maintain improved recommendations. We release our code at
https://github.com/Chrystalii/CERec