建议的深度因果推理

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yaochen Zhu, Jing Yi, Jiayi Xie, Zhenzhong Chen
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引用次数: 0

摘要

传统的推荐系统旨在根据从人群中观察到的评分来估算用户对项目的评分。与所有观察性研究一样,隐藏的混杂因素(即同时影响项目暴露和用户评分的因素)会导致估算出现系统性偏差。因此,推荐中引入了因果推理,以解决未观察到的混杂因素的影响。考虑到推荐中的混杂因素通常在项目之间共享,因此属于多原因混杂因素,我们将推荐建模为多原因多结果(MCMO)推断问题。具体来说,为了弥补混杂偏差,我们估计了用户特定的潜在变量,使项目暴露成为独立的伯努利试验。生成分布由具有因子化逻辑似然的 DNN 参数化,并通过变异推理估计难以处理的后验。在温和的假设条件下,将这些因素作为替代混杂因素进行控制,可以消除多原因混杂因素带来的偏差。此外,我们还表明,MCMO 模型可能会导致高方差,原因是与高维治疗空间相关的观察结果很少。因此,我们从理论上证明,将用户特征作为预处理变量来控制,可以大大提高样本效率,减轻过度拟合。在模拟和真实世界数据集上进行的实证研究表明,与最先进的因果推荐器相比,所提出的深度因果推荐器对未观察到的混杂因素表现出更强的鲁棒性。代码和数据集发布于 https://github.com/yaochenzhu/Deep-Deconf。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Causal Reasoning for Recommendations

Traditional recommender systems aim to estimate a user’s rating to an item based on observed ratings from the population. As with all observational studies, hidden confounders, which are factors that affect both item exposures and user ratings, lead to a systematic bias in the estimation. Consequently, causal inference has been introduced in recommendations to address the influence of unobserved confounders. Observing that confounders in recommendations are usually shared among items and are therefore multi-cause confounders, we model the recommendation as a multi-cause multi-outcome (MCMO) inference problem. Specifically, to remedy the confounding bias, we estimate user-specific latent variables that render the item exposures independent Bernoulli trials. The generative distribution is parameterized by a DNN with factorized logistic likelihood and the intractable posteriors are estimated by variational inference. Controlling these factors as substitute confounders, under mild assumptions, can eliminate the bias incurred by multi-cause confounders. Furthermore, we show that MCMO modeling may lead to high variance due to scarce observations associated with the high-dimensional treatment space. Therefore, we theoretically demonstrate that controlling user features as pre-treatment variables can substantially improve sample efficiency and alleviate overfitting. Empirical studies on both simulated and real-world datasets demonstrate that the proposed deep causal recommender shows more robustness to unobserved confounders than state-of-the-art causal recommenders. Codes and datasets are released at https://github.com/yaochenzhu/Deep-Deconf.

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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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