来自今天的爱可可AI前沿推介

[IR] Causal Inference for Recommendation: Foundations, Methods and Applications

S Xu, J Ji, Y Li, Y Ge, J Tan, Y Zhang
[Rutgers University]

面向推荐的因果推断:基础、方法和应用

要点:

  1. 全面回顾推荐系统中因果推理的相关文献,包括推荐系统和因果推断的基本概念及其关系;
  2. 讨论完全依赖推荐系统相关性可能产生的实际问题,如公平性、可解释性、鲁棒性、偏差、回声室和可控性问题;
  3. 探索推荐系统不同问题的因果方法的现有工作,包括可解释性、公平性、鲁棒性、基于提升和推荐中的公正性。

一句话总结:
全面调研面向推荐系统的因果推断的相关文献,包括基本概念、实际问题、关于各种问题的因果方法的现有工作,以及开放式问题和未来方向。

摘要:
推荐系统是各种个性化服务的重要而强大的工具。传统上,这些系统使用数据挖掘和机器学习技术根据数据中的相关性提出建议。然而,仅仅依靠相关性而不考虑潜在的因果机制可能会导致各种实际问题,如公平性、可解释性、鲁棒性、偏差、回声室和可控性问题。因此,相关领域的研究人员已经开始将因果纳入推荐系统,以解决这些问题。本综述回顾了关于推荐系统因果推断的现有文献。讨论了推荐系统和因果推断的基本概念及其关系,回顾了推荐系统中不同问题的因果方法的现有工作。最后,讨论了建议因果推断领域的未决问题和未来方向。

Recommender systems are important and powerful tools for various personalized services. Traditionally, these systems use data mining and machine learning techniques to make recommendations based on correlations found in the data. However, relying solely on correlation without considering the underlying causal mechanism may lead to various practical issues such as fairness, explainability, robustness, bias, echo chamber and controllability problems. Therefore, researchers in related area have begun incorporating causality into recommendation systems to address these issues. In this survey, we review the existing literature on causal inference in recommender systems. We discuss the fundamental concepts of both recommender systems and causal inference as well as their relationship, and review the existing work on causal methods for different problems in recommender systems. Finally, we discuss open problems and future directions in the field of causal inference for recommendations.

论文链接:https://arxiv.org/abs/2301.04016
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