发表机构
Korea University; University of Illinois Urbana-Champaign; MBZUAI（高丽大学; 伊利诺伊大学厄巴纳-香槟分校; 穆罕默德·本·扎耶德人工智能大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多行为推荐中辅助信号缺失与不可靠的问题，提出环境条件的BOAR框架，实验显示其在HR@10指标上优于现有最优基线，对无辅助观测目标物品提升显著。
AI 中文摘要
多行为推荐（MBR）利用点击、加入购物车等辅助行为信号来提升购买等目标行为的预测效果。尽管近期基于图神经网络的方法通过系统性传播辅助行为信号取得了优异性能，但仍面临辅助行为固有的两大核心挑战：（1）缺失辅助信号，阻碍对无辅助观测物品的泛化；（2）不可靠辅助信号，放大与目标行为不一致的噪声。为统一解决这些挑战，本文提出BOAR框架，这是一种环境条件下的多行为推荐框架，通过两个以辅助可观测性为条件的互补模块应对上述问题。大量实验表明，BOAR始终优于现有最优基线，整体HR@10指标提升最高达7.82%，对无辅助观测的目标物品提升最高达44.2%，凸显其捕捉观测到的辅助关系之外隐藏偏好的能力。代码可访问：this https URL。
英文摘要
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.
CommentsAccepted at CIKM 2026 (35th ACM International Conference on Information and Knowledge Management). 11 pages, 7 figures, 4 tables
