A fundamental challenge of recommendation systems (RS) is understanding the causal dynamics underlying users' decision making. Most existing literature addresses this problem by using causal structures inferred from domain knowledge. However, there are numerous phenomenons where domain knowledge is insufficient, and the causal mechanisms must be learnt from the feedback data. Discovering the causal mechanism from RS feedback data is both novel and challenging, since RS itself is a source of intervention that can influence both the users' exposure and their willingness to interact. Also for this reason, most existing solutions become inappropriate since they require data collected free from any RS. In this paper, we first formulate the underlying causal mechanism as a causal structural model and describe a general causal structure learning framework grounded in the real-world working mechanism of RS. The essence of our approach is to acknowledge the unknown nature of RS intervention. We then derive the learning objective from our framework and propose an augmented Lagrangian solver for efficient optimization. We conduct both simulation and real-world experiments to demonstrate how our approach compares favorably to existing solutions, together with the empirical analysis from sensitivity and ablation studies.
翻译:建议系统(RS)的根本挑战是了解用户决策所依据的因果动态。大多数现有文献都利用从领域知识中推断的因果结构来解决这个问题。然而,有许多现象,领域知识不足,必须从反馈数据中学习因果机制。从RS反馈数据中发现因果机制既新颖又具有挑战性,因为RS本身是一个干预来源,既能影响用户的接触,又能影响其互动意愿。同样出于这一原因,大多数现有解决方案变得不适当,因为它们需要从任何RS免费收集的数据。在本文中,我们首先将根本因果机制发展为因果结构模型,并描述基于RS现实世界工作机制的一般因果结构学习框架。我们方法的实质是承认RS干预的未知性质。然后,我们从我们的框架中得出学习目标,并提出一个强化的Lagranchian解决方案,以便高效优化。我们进行模拟和现实世界实验,以证明我们的方法如何优于现有解决方案,同时进行敏感度和对比研究的经验分析。