Luo Huishi, Zhuang Fuzhen, Xie Ruobing, Zhu Hengshu, Wang Deqing, An Zhulin, Xu Yongjun
Institute of Artificial Intelligence, Beihang University, Beijing 100191, China.
Zhongguancun Laboratory, Beijing 100094, China.
Innovation (Camb). 2024 Feb 8;5(2):100590. doi: 10.1016/j.xinn.2024.100590. eCollection 2024 Mar 4.
Causal inference has recently garnered significant interest among recommender system (RS) researchers due to its ability to dissect cause-and-effect relationships and its broad applicability across multiple fields. It offers a framework to model the causality in RSs such as confounding effects and deal with counterfactual problems such as offline policy evaluation and data augmentation. Although there are already some valuable surveys on causal recommendations, they typically classify approaches based on the practical issues faced in RS, a classification that may disperse and fragment the unified causal theories. Considering RS researchers' unfamiliarity with causality, it is necessary yet challenging to comprehensively review relevant studies from a coherent causal theoretical perspective, thereby facilitating a deeper integration of causal inference in RS. This survey provides a systematic review of up-to-date papers in this area from a causal theory standpoint and traces the evolutionary development of RS methods within the same causal strategy. First, we introduce the fundamental concepts of causal inference as the basis of the following review. Subsequently, we propose a novel theory-driven taxonomy, categorizing existing methods based on the causal theory employed, namely those based on the potential outcome framework, the structural causal model, and general counterfactuals. The review then delves into the technical details of how existing methods apply causal inference to address particular recommender issues. Finally, we highlight some promising directions for future research in this field. Representative papers and open-source resources will be progressively available at https://github.com/Chrissie-Law/Causal-Inference-for-Recommendation.
因果推断最近在推荐系统（RS）研究人员中引起了极大的兴趣，因为它能够剖析因果关系，并且在多个领域具有广泛的适用性。它提供了一个框架，用于对推荐系统中的因果关系（如混杂效应）进行建模，并处理诸如离线策略评估和数据增强等反事实问题。尽管已经有一些关于因果推荐的有价值的综述，但它们通常根据推荐系统中面临的实际问题对方法进行分类，这种分类可能会分散和割裂统一的因果理论。考虑到推荐系统研究人员对因果关系并不熟悉，从连贯的因果理论角度全面回顾相关研究既必要又具有挑战性，从而促进因果推断在推荐系统中的更深入整合。本综述从因果理论的角度对该领域的最新论文进行了系统回顾，并追溯了同一因果策略下推荐系统方法的演变发展。首先，我们介绍因果推断的基本概念，作为后续综述的基础。随后，我们提出了一种新颖的理论驱动分类法，根据所采用的因果理论对现有方法进行分类，即基于潜在结果框架、结构因果模型和一般反事实的方法。然后，综述深入探讨了现有方法如何应用因果推断来解决特定推荐问题的技术细节。最后，我们强调了该领域未来研究的一些有前景的方向。代表性论文和开源资源将陆续在https://github.com/Chrissie-Law/Causal-Inference-for-Recommendation上提供。
