通过学习可识别的潜在混杂因素消除推荐系统的偏差
Debiasing Recommendation by Learning Identifiable Latent Confounders
AI总结：
针对推荐系统中未测量混杂因素引发的偏差及现有方法无法保证反事实反馈可识别的问题，提出基于近端因果推断的可识别去混杂器iDCF框架，经多数据集实验验证其有效性与鲁棒性。
摘要：
推荐系统旨在预测用户对未曝光物品的反馈。由于存在可同时影响用户曝光行为与反馈的未测量变量（例如用户的社会经济地位），系统会产生混杂偏差。现有方法要么对这些未测量变量作出站不住脚的假设，要么直接从用户曝光数据推断潜在混杂因素，但都无法保证反事实反馈的可识别性，可能导致预测存在偏差。本研究提出一种名为可识别去混杂器（identifiable deconfounder, iDCF）的新方法，利用一组代理变量（例如观测到的用户特征）解决上述不可识别问题。iDCF是一个通用的去混杂推荐框架，采用近端因果推断方法推断未测量混杂因素，并在理论保证下识别反事实反馈。在多个真实世界与合成数据集上开展的大量实验，验证了所提方法的有效性与鲁棒性。
英文摘要：
Recommendation systems aim to predict users' feedback on items not exposed to them. Confounding bias arises due to the presence of unmeasured variables (e.g., the socio-economic status of a user) that can affect both a user's exposure and feedback. Existing methods either (1) make untenable assumptions about these unmeasured variables or (2) directly infer latent confounders from users' exposure. However, they cannot guarantee the identification of counterfactual feedback, which can lead to biased predictions. In this work, we propose a novel method, i.e., identifiable deconfounder (iDCF), which leverages a set of proxy variables (e.g., observed user features) to resolve the aforementioned non-identification issue. The proposed iDCF is a general deconfounded recommendation framework that applies proximal causal inference to infer the unmeasured confounders and identify the counterfactual feedback with theoretical guarantees. Extensive experiments on various real-world and synthetic datasets verify the proposed method's effectiveness and robustness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
