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.

翻译：推荐系统从业者正面临越来越大的解释推荐结果的压力。我们探索利用反事实逻辑解释推荐结果，即"如果你未与以下物品交互，我们便不会推荐该物品"。与传统解释逻辑相比，反事实解释更易于理解、更具技术可验证性，且在赋予用户对推荐的控制权方面包含更丰富的信息。生成此类解释的主要挑战在于计算成本高昂，因为需要反复重新训练模型来获取移除用户历史记录对推荐结果产生的影响。我们提出一种基于学习的框架来生成反事实解释，其核心思想是训练替代模型学习移除用户历史子集对推荐结果的影响。为此，我们首先人工模拟删除历史子集后推荐结果的反事实输出，然后训练替代模型学习历史删除与推荐结果相应变化之间的映射关系。最终，为生成解释，我们寻找替代模型预测最可能移除该推荐的历史子集。通过离线实验和在线用户研究，我们证明本方法相比基线方法能生成反事实有效性更高且用户满意度更佳的解释。