Causal Learning for Trustworthy Recommender Systems: A Survey
CoRR(2024)
摘要
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.
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