Counterfactual Graph Convolutional Learning for Personalized Recommendation

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Meng Jian, Yulong Bai, Xusong Fu, Jingjing Guo, Ge Shi, Lifang Wu
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Abstract

Recently, recommender systems have witnessed the fast evolution of Internet services. However, it suffers hugely from inherent bias and sparsity issues in interactions. The conventional uniform embedding learning policies fail to utilize the imbalanced interaction clue and produce suboptimal representations to users and items for recommendation. Towards the issue, this work is dedicated to bias-aware embedding learning in a decomposed manner and proposes a counterfactual graph convolutional learning (CGCL) model for personalized recommendation. Instead of debiasing with uniform interaction sampling, we follow the natural interaction bias to model users’ interests with a counterfactual hypothesis. CGCL introduces bias-aware counterfactual masking on interactions to distinguish the effects between majority and minority causes on the counterfactual gap. It forms multiple counterfactual worlds to extract users’ interests in minority causes compared to the factual world. Concretely, users and items are represented with a causal decomposed embedding of majority and minority interests for recommendation. Experiments show that the proposed CGCL is superior to the state-of-the-art baselines. The performance illustrates the rationality of the counterfactual hypothesis in bias-aware embedding learning for personalized recommendation.

用于个性化推荐的反事实图卷积学习
最近,推荐系统见证了互联网服务的快速发展。然而,交互中固有的偏差和稀疏性问题使其受到严重影响。传统的统一嵌入学习策略无法利用不平衡的交互线索,并产生次优的用户和项目推荐表征。针对这一问题,本研究致力于以分解的方式进行偏差感知嵌入学习,并提出了一种用于个性化推荐的反事实图卷积学习(CGCL)模型。我们不采用统一交互采样去除法,而是遵循自然交互偏差,以反事实假设来模拟用户兴趣。CGCL 对交互引入了偏差感知的反事实掩蔽,以区分多数原因和少数原因对反事实差距的影响。与事实世界相比,它形成了多个反事实世界,以提取用户对少数原因的兴趣。具体来说,用户和项目是通过多数人和少数人利益的因果分解嵌入来表示的,以便进行推荐。实验表明,建议的 CGCL 优于最先进的基线。其表现说明了反事实假设在用于个性化推荐的偏差感知嵌入学习中的合理性。
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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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