Beyond Observed Auxiliary Relations: Environment-Conditioned Modeling for Multi-Behavior Recommendation
2026-08-24 • Artificial Intelligence
Artificial IntelligenceMachine Learning
AI summaryⓘ
The authors study how to improve recommendations by using extra behaviors like clicks or add-to-cart actions to predict purchases. They point out two problems: sometimes these extra behaviors are missing, and sometimes they are noisy or unreliable. To fix this, they created BOAR, a system that adapts based on how much extra behavior data is available and how trustworthy it is. Their experiments show BOAR works better than existing methods, especially for items lacking extra behavior information.
multi-behavior recommendationgraph neural networksauxiliary signalsmissing datanoisy datarecommendation systemsuser behaviorHR@10target behavior prediction
Authors
Seunghan Lee, Hyunsik Yoo, Jian Kang, Susik Yoon, SeongKu Kang
Abstract
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.