By providing explanations for users and system designers to facilitate better understanding and decision making, explainable recommendation has been an important research problem. In this paper, we propose Counterfactual Explainable Recommendation (CountER), which takes the insights of counterfactual reasoning from causal inference for explainable recommendation. CountER is able to formulate the complexity and the strength of explanations, and it adopts a counterfactual learning framework to seek simple (low complexity) and effective (high strength) explanations for the model decision. Technically, for each item recommended to each user, CountER formulates a joint optimization problem to generate minimal changes on the item aspects so as to create a counterfactual item, such that the recommendation decision on the counterfactual item is reversed. These altered aspects constitute the explanation of why the original item is recommended. The counterfactual explanation helps both the users for better understanding and the system designers for better model debugging. Another contribution of the work is the evaluation of explainable recommendation, which has been a challenging task. Fortunately, counterfactual explanations are very suitable for standard quantitative evaluation. To measure the explanation quality, we design two types of evaluation metrics, one from user's perspective (i.e. why the user likes the item), and the other from model's perspective (i.e. why the item is recommended by the model). We apply our counterfactual learning algorithm on a black-box recommender system and evaluate the generated explanations on five real-world datasets. Results show that our model generates more accurate and effective explanations than state-of-the-art explainable recommendation models.

翻译：通过为用户和系统设计者提供解释以促进更好的理解与决策，可解释推荐已成为重要的研究课题。本文提出反事实可解释推荐（CountER），该模型从因果推断中引入反事实推理思想用于可解释推荐。CountER能够形式化解释的复杂度与强度，并采用反事实学习框架为模型决策寻求简单（低复杂度）且有效（高强度）的解释。技术上，针对推荐给每个用户的每个物品，CountER构建联合优化问题，通过对物品属性进行最小化修改生成反事实物品，使得该反事实物品的推荐决策发生反转。这些被修改的属性即构成原物品被推荐的原因解释。反事实解释既有助于用户更好地理解推荐结果，又能帮助系统设计者进行模型调试。本研究的另一贡献在于对可解释推荐这一难题的评估。反事实解释天然适用于标准化定量评估。为衡量解释质量，我们设计了两类评估指标：一类从用户视角（即用户为何喜欢该物品），另一类从模型视角（即模型为何推荐该物品）。我们将反事实学习算法应用于黑盒推荐系统，并在五个真实数据集上评估生成的解释。结果表明，与现有最优可解释推荐模型相比，我们的模型能生成更准确且更有效的解释。