推荐系统中的因果推理:调查与未来方向

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Chen Gao, Yu Zheng, Wenjie Wang, Fuli Feng, Xiangnan He, Yong Li
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引用次数: 27

摘要

如今,推荐系统已成为信息过滤的关键。现有的推荐系统根据数据的相关性来提取用户偏好,如协同过滤中的行为相关性,点击率预测中的特征-特征或特征-行为相关性。然而,不幸的是,现实世界是由因果关系驱动的,而不仅仅是相关性,相关性并不意味着因果关系。例如,推荐系统可能会在用户购买手机后向其推荐电池充电器,而后者可能是前者的原因;这种因果关系无法逆转。最近,为了解决这个问题,推荐系统的研究人员开始利用因果推理来提取因果关系,从而增强推荐系统的功能。在本调查中,我们将对基于因果推理的推荐文献进行全面回顾。首先,我们介绍了推荐系统和因果推理的基本概念,作为后续内容的基础。然后,我们强调了非因果关系推荐系统所面临的典型问题。随后,我们根据因果推理可应对的三方面挑战的分类法,全面回顾了基于因果推理的推荐系统方面的现有工作。最后,我们讨论了这一关键研究领域的未决问题,并提出了未来可能开展的重要工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Causal Inference in Recommender Systems: A Survey and Future Directions

Recommender systems have become crucial in information filtering nowadays. Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction. However, unfortunately, the real world is driven by causality, not just correlation, and correlation does not imply causation. For instance, recommender systems might recommend a battery charger to a user after buying a phone, where the latter can serve as the cause of the former; such a causal relation cannot be reversed. Recently, to address this, researchers in recommender systems have begun utilizing causal inference to extract causality, thereby enhancing the recommender system. In this survey, we offer a comprehensive review of the literature on causal inference-based recommendation. Initially, we introduce the fundamental concepts of both recommender system and causal inference as the foundation for subsequent content. We then highlight the typical issues faced by non-causality recommender system. Following that, we thoroughly review the existing work on causal inference-based recommender systems, based on a taxonomy of three-aspect challenges that causal inference can address. Finally, we discuss the open problems in this critical research area and suggest important potential future works.

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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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