Causal Learning for Trustworthy Recommender Systems: A Survey

TL;DR

This survey reviews how causal learning can enhance Trustworthy Recommender Systems by addressing biases, improving fairness, robustness, and explainability, and highlights current methods, challenges, and future research directions.

Contribution

It provides a comprehensive overview of causality-based approaches in Trustworthy Recommender Systems, filling a gap in recent survey literature.

Findings

Causality-oriented TRS offers advantages over correlation-based methods.

Identification of trustworthiness challenges at different stages of TRS.

Classification of causal methods applied in TRS.

Abstract

Recommender Systems (RS) have significantly advanced online content filtering and personalized decision-making. However, emerging vulnerabilities in RS have catalyzed a paradigm shift towards Trustworthy RS (TRS). Despite substantial progress on TRS, most efforts focus on data correlations while overlooking the fundamental causal nature of recommendations. This drawback hinders TRS from identifying the root cause of 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 noise while offering insightful explanations for TRS. However, there is a lack of timely and dedicated surveys 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.