Plausible Counterfactual Explanations of Recommendations
This addresses the need for more effective explanations in recommender systems, which can enhance user trust and compliance, though it is incremental as it builds on existing counterfactual explanation methods.
The paper tackled the problem of generating plausible counterfactual explanations for recommendations in recommender systems, and the result was a method that achieved high plausibility as evaluated numerically and through a user study.
Explanations play a variety of roles in various recommender systems, from a legally mandated afterthought, through an integral element of user experience, to a key to persuasiveness. A natural and useful form of an explanation is the Counterfactual Explanation (CE). We present a method for generating highly plausible CEs in recommender systems and evaluate it both numerically and with a user study.