Dynamic Causal Collaborative Filtering
Dynamic Causal Collaborative Filtering
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DOI：
10.1145/3511808.3557300
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Shuyuan Xu;Juntao Tan;Zuohui Fu;Jianchao Ji;Shelby Heinecke;Yongfeng Zhang
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文献类型：
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作者：
Shuyuan Xu;Juntao Tan;Zuohui Fu;Jianchao Ji;Shelby Heinecke;Yongfeng Zhang
因果图作为一种有效的因果建模工具，通常被认为是一个有向无环图。然而，推荐系统通常涉及反馈循环，定义为推荐项目的循环过程，将用户反馈纳入模型更新，并重复该过程。因此，重要的是将循环纳入因果图中，以准确地建模推荐系统的动态和迭代数据生成过程。然而，反馈循环并不总是有益的，因为随着时间的推移，它们可能会鼓励越来越窄的内容曝光，如果不加注意，可能会导致回音室。因此，重要的是要了解建议何时会导致回音室，以及如何在不损害建议性能的情况下减轻回音室。在本文中，我们设计了一个因果图的循环来描述推荐的动态过程。然后，我们采取马尔可夫过程来分析回波室的数学性质，如条件，导致回波室。在理论分析的启发下，提出了一种动态因果协同过滤（$\partial$CCF）模型，该模型基于后门调整估计用户对项目的干预后偏好，并通过反事实推理消除回音室。在真实数据集上进行了多个实验，结果表明，我们的框架可以比其他最先进的框架更好地减轻回声室，同时实现与基本推荐模型相当的推荐性能。
Causal graph, as an effective and powerful tool for causal modeling, is usually assumed as a Directed Acyclic Graph (DAG). However, recommender systems usually involve feedback loops, defined as the cyclic process of recommending items, incorporating user feedback in model updates, and repeating the procedure. As a result, it is important to incorporate loops into the causal graphs to accurately model the dynamic and iterative data generation process for recommender systems. However, feedback loops are not always beneficial since over time they may encourage more and more narrowed content exposure, which if left unattended, may results in echo chambers. As a result, it is important to understand when the recommendations will lead to echo chambers and how to mitigate echo chambers without hurting the recommendation performance. In this paper, we design a causal graph with loops to describe the dynamic process of recommendation. We then take Markov process to analyze the mathematical properties of echo chamber such as the conditions that lead to echo chambers. Inspired by the theoretical analysis, we propose a Dynamic Causal Collaborative Filtering ($\partial$CCF) model, which estimates users' post-intervention preference on items based on back-door adjustment and mitigates echo chamber with counterfactual reasoning. Multiple experiments are conducted on real-world datasets and results show that our framework can mitigate echo chambers better than other state-of-the-art frameworks while achieving comparable recommendation performance with the base recommendation models.