Multi-behavior recommendation (MBR) aims to improve the performance w.r.t. the target behavior (i.e., purchase) by leveraging auxiliary behaviors (e.g., click, favourite). However, in real-world scenarios, a recommendation method often needs to process different types of behaviors and generate personalized lists for each task (i.e., each behavior type). Such a new recommendation problem is referred to as multi-behavior multi-task recommendation (MMR). So far, the most powerful MBR methods usually model multi-behavior interactions using a cascading graph paradigm. Although significant progress has been made in optimizing the performance of the target behavior, it often neglects the performance of auxiliary behaviors. To compensate for the deficiencies of the cascading paradigm, we propose a novel solution for MMR, i.e., behavior-informed graph embedding learning (BiGEL). Specifically, we first obtain a set of behavior-aware embeddings by using a cascading graph paradigm. Subsequently, we introduce three key modules to improve the performance of the model. The cascading gated feedback (CGF) module enables a feedback-driven optimization process by integrating feedback from the target behavior to refine the auxiliary behaviors preferences. The global context enhancement (GCE) module integrates the global context to maintain the user's overall preferences, preventing the loss of key preferences due to individual behavior graph modeling. Finally, the contrastive preference alignment (CPA) module addresses the potential changes in user preferences during the cascading process by aligning the preferences of the target behaviors with the global preferences through contrastive learning. Extensive experiments on two real-world datasets demonstrate the effectiveness of our BiGEL compared with ten very competitive methods.

翻译：多行为推荐旨在通过利用辅助行为（如点击、收藏）来提升目标行为（如购买）的推荐性能。然而，在实际场景中，推荐方法通常需要处理不同类型的行为，并为每个任务（即每种行为类型）生成个性化列表。这一新的推荐问题被称为多行为多任务推荐。迄今为止，最强大的多行为推荐方法通常采用级联图范式来建模多行为交互。尽管在优化目标行为性能方面已取得显著进展，但此类方法往往忽视了辅助行为的性能。为弥补级联范式的不足，我们提出了一种面向多行为多任务推荐的新颖解决方案，即基于行为感知的图嵌入学习。具体而言，我们首先通过级联图范式获得一组行为感知嵌入。随后，我们引入了三个关键模块以提升模型性能。级联门控反馈模块通过整合来自目标行为的反馈来优化辅助行为偏好，从而实现反馈驱动的优化过程。全局上下文增强模块整合全局上下文以维持用户的整体偏好，防止因单个行为图建模而导致关键偏好丢失。最后，对比偏好对齐模块通过对比学习将目标行为偏好与全局偏好对齐，以解决级联过程中用户偏好可能发生改变的问题。在两个真实数据集上的大量实验表明，与十种极具竞争力的方法相比，我们的BiGEL模型具有显著优势。