解决多行为推荐中辅助行为数据缺失与噪声问题，提升冷门商品推荐效果。
Beyond Observed Auxiliary Relations: Environment-Conditioned Modeling for Multi-Behavior Recommendation
- 通过环境条件化机制动态处理辅助行为的可观测性。
- 在无辅助行为的商品上提升44.2%的推荐准确率，整体最高增益7.82%。
- 适合需要精准捕捉隐含偏好的推荐系统开发者使用。
多行为推荐（MBR）利用点击、加购等辅助行为信号来提升购买行为预测。尽管基于图神经网络的方法能有效传播辅助信号，但仍面临两大根本挑战：一是辅助信号缺失，限制了对无观测商品的泛化能力；二是辅助信号不可靠，会引入与目标行为不一致的噪声。为此，我们提出环境条件化多行为推荐框架BOAR，通过两个依赖辅助可观测性的模块，统一应对上述问题。大量实验表明，BOAR持续优于现有最佳基线，在HR@10上整体提升最高达7.82%，对无辅助观测商品的提升高达44.2%，验证了其捕捉隐藏偏好、超越已观测辅助关系的能力。代码已开源：https://github.com/LSH0411/BOAR。
原文摘要 · Abstract (English)
Multi-behavior recommendation (MBR) leverages auxiliary behavioral signals, such as clicks and add-to-cart, to enhance target behavior prediction like purchases. While recent graph neural network-based approaches have achieved strong performance by systematically propagating auxiliary behavior signals, they still suffer from two fundamental challenges inherent to auxiliary behaviors: (1) missing auxiliary signals, which hinder generalization to items without auxiliary observations, and (2) unreliable auxiliary signals, which amplify noise misaligned with the target behavior. To address these challenges in a unified manner, we propose BOAR, an environment-conditioned MBR framework that addresses missing and unreliable auxiliary signals through two complementary modules conditioned on auxiliary observability. Extensive experiments demonstrate that BOAR consistently outperforms state-of-the-art baselines, achieving up to 7.82% gains in HR@10 overall and up to 44.2% gains for target items without auxiliary observations, highlighting its ability to capture hidden preferences beyond observed auxiliary relations. Our code is available at: https://github.com/LSH0411/BOAR.
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