  Understanding why specific items are recommended to users can significantly
increase their trust and satisfaction in the system. While neural recommenders
have become the state-of-the-art in recent years, the complexity of deep models
still makes the generation of tangible explanations for end users a challenging
problem. Existing methods are usually based on attention distributions over a
variety of features, which are still questionable regarding their suitability
as explanations, and rather unwieldy to grasp for an end user. Counterfactual
explanations based on a small set of the user's own actions have been shown to
be an acceptable solution to the tangibility problem. However, current work on
such counterfactuals cannot be readily applied to neural models. In this work,
we propose ACCENT, the first general framework for finding counterfactual
explanations for neural recommenders. It extends recently-proposed influence
functions for identifying training points most relevant to a recommendation,
from a single to a pair of items, while deducing a counterfactual set in an
iterative process. We use ACCENT to generate counterfactual explanations for
two popular neural models, Neural Collaborative Filtering (NCF) and Relational
Collaborative Filtering (RCF), and demonstrate its feasibility on a sample of
the popular MovieLens 100K dataset.
