  Recommender system practitioners are facing increasing pressure to explain
recommendations. We explore how to explain recommendations using counterfactual
logic, i.e. "Had you not interacted with the following items, we would not
recommend it." Compared to the traditional explanation logic, counterfactual
explanations are easier to understand, more technically verifiable, and more
informative in terms of giving users control over recommendations. The major
challenge of generating such explanations is the computational cost because it
requires repeatedly retraining the models to obtain the effect on a
recommendation caused by the absence of user history. We propose a
learning-based framework to generate counterfactual explanations. The key idea
is to train a surrogate model to learn the effect of removing a subset of user
history on the recommendation. To this end, we first artificially simulate the
counterfactual outcomes on the recommendation after deleting subsets of
history. Then we train a surrogate model to learn the mapping between a history
deletion and the corresponding change of the recommendation caused by the
deletion. Finally, to generate an explanation, we find the history subset
predicted by the surrogate model that is most likely to remove the
recommendation. Through offline experiments and online user studies, we show
our method, compared to baselines, can generate explanations that are more
counterfactually valid and more satisfactory considered by users.
