Causal Learning for Trustworthy Recommender Systems: A Survey
<|reference_start|>Causal Learning for Trustworthy Recommender Systems: A Survey: Recommender Systems (RS) have significantly advanced online content discovery and personalized decision-making. However, emerging vulnerabilities in RS have catalyzed a paradigm shift towards Trustworthy RS (TRS). Despite numerous progress on TRS, most of them focus on data correlations while overlooking the fundamental causal nature in recommendation. This drawback hinders TRS from identifying the cause in addressing trustworthiness issues, leading to limited fairness, robustness, and explainability. To bridge this gap, causal learning emerges as a class of promising methods to augment TRS. These methods, grounded in reliable causality, excel in mitigating various biases and noises while offering insightful explanations for TRS. However, there lacks a timely survey in this vibrant area. This paper creates an overview of TRS from the perspective of causal learning. We begin by presenting the advantages and common procedures of Causality-oriented TRS (CTRS). Then, we identify potential trustworthiness challenges at each stage and link them to viable causal solutions, followed by a classification of CTRS methods. Finally, we discuss several future directions for advancing this field.<|reference_end|>
@article{li2024causal,
    title={Causal Learning for Trustworthy Recommender Systems: A Survey},
    author={Jin Li, Shoujin Wang, Qi Zhang, Longbing Cao, Fang Chen, Xiuzhen
  Zhang, Dietmar Jannach and Charu C. Aggarwal},
    journal={arXiv preprint arXiv:2402.08241},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08241},
    primaryClass={cs.IR}
}