A Survey on Debiasing Recommendation Based on Causal Inference

Recommender systems play a vital role in addressing information overload by learning user preferences from historical interaction data and thereby providing personalized recommendations. However, various biases in recommendation systems hinder the accurate modeling of users' true preferences, liming the improvement of recommendation performance. Recently, the development of causal inference theory has provided robust support for analyzing and resolving bias problems in recommender systems. Causal inference, a statistical method aimed at identifying and estimating causal effects between variables from observational data, assists and eliminating biases through the construction and analysis of causal models, enhancing the accuracy of iitting user preferences. Applying causal inference to debiasing tasks in recommender systems has achieved significant success, effectively mitigating bias while also enhancing accuracy and reliability. This paper provides a comprehensive review of the research on debiasing recommendations based on causal inference. that bias occurs at various stages of recommender systems, we classify the sources of bias according to the three stages of recommender systems: data, algorithms, and evaluation. We also summarize the manifestations of bias at each stage and their impact on recommendations. Based on the study of debiasing recommendations from a causal perspective, we first outline the principles and key methods of causal inference. This establishes the connection between causal inference and debiasing recommendation, providing insights into mitigating bias. Then we systematically organize and analyze debiasing strategies for recommender systems at the data, algorithm, and evaluation stages based on causal inference techniques. For debiasing methods at the data stage, there are primarily two strategies based on how the data is utilized: counterfactual construction-based methods generate synthetic data points to simulate what might happen under different scenarios, helping to uncover hidden biases ; unbiased data-based methods involve collecting data that is free from the common biases present in real-world datasets. For debiasing methods at the algorithm stage, there are primarily three strategies based on different causal techniques :causal representation learning-based methods aim to learn representations of the data that are invariant to biases; causal intervention-based methods directly manipulate variables to observe changes and infer causal relationships ; counterfactual reasoning-based methods involve comparing actual outcomes with hypothetical scenarios to identify and correct biases. For debiasing methods at the evaluation stage, there are primarily two strategies based on the correlation and optimization of unbiased estimates: inverse property scoring-based methods adjust for the probability of receiving a particular treatment, helping to balance the dataset ; doubly robust-based methods combine property score weighting with outcome modeling to improve the robustness and accuracy of bias correction. Currently, recommender systems based on causal inference represent a novel and challenging research field. This paper summarizes several open research directions, including debiasing recommendation methods based on causal discovery, a general causal-based debiasing recommendation framework, robust debiasing methods based on causal inference, addressing bias issues in dynamic environments using causal approaches, and the construction of datasets for causal debiasing recommendations. Finally, we summarize this paper and provide an outlook on the research of debiasing recommendations based on causal inference from the perspectives of application needs and technological development. © 2024 Science Press. All rights reserved.