论文标题
清晰：神经推荐人注意的因果解释
CLEAR: Causal Explanations from Attention in Neural Recommenders
论文作者
论文摘要
我们提出了一种清晰的方法，即在潜在的混杂因素的存在下，从预先训练的基于注意力的推荐人的注意力中，可能存在潜在的混杂因素。这些因果图描述了用户行为，在注意力捕获的上下文中，可以为建议提供反事实的解释。从本质上讲，这些因果图允许在任何特定会话中回答“为什么”问题。使用经验评估，我们表明，与天真使用注意力权重解释输入输出关系相比，Clear发现的反事实解释较短，并且在原始的Top-K建议中排名更高。
We present CLEAR, a method for learning session-specific causal graphs, in the possible presence of latent confounders, from attention in pre-trained attention-based recommenders. These causal graphs describe user behavior, within the context captured by attention, and can provide a counterfactual explanation for a recommendation. In essence, these causal graphs allow answering "why" questions uniquely for any specific session. Using empirical evaluations we show that, compared to naively using attention weights to explain input-output relations, counterfactual explanations found by CLEAR are shorter and an alternative recommendation is ranked higher in the original top-k recommendations.
