Modern recommender systems often deal with a variety of user interactions, e.g., click, forward, purchase, etc., which requires the underlying recommender engines to fully understand and leverage multi-behavior data from users. Despite recent efforts towards making use of heterogeneous data, multi-behavior recommendation still faces great challenges. Firstly, sparse target signals and noisy auxiliary interactions remain an issue. Secondly, existing methods utilizing self-supervised learning (SSL) to tackle the data sparsity neglect the serious optimization imbalance between the SSL task and the target task. Hence, we propose a Multi-Behavior Self-Supervised Learning (MBSSL) framework together with an adaptive optimization method. Specifically, we devise a behavior-aware graph neural network incorporating the self-attention mechanism to capture behavior multiplicity and dependencies. To increase the robustness to data sparsity under the target behavior and noisy interactions from auxiliary behaviors, we propose a novel self-supervised learning paradigm to conduct node self-discrimination at both inter-behavior and intra-behavior levels. In addition, we develop a customized optimization strategy through hybrid manipulation on gradients to adaptively balance the self-supervised learning task and the main supervised recommendation task. Extensive experiments on five real-world datasets demonstrate the consistent improvements obtained by MBSSL over ten state-of-the art (SOTA) baselines. We release our model implementation at: https://github.com/Scofield666/MBSSL.git.

翻译：现代推荐系统通常需要处理多种用户交互行为（如点击、转发、购买等），要求底层推荐引擎充分理解并利用用户的多行为数据。尽管近期已有研究致力于利用异构数据，但多行为推荐仍面临巨大挑战。首先，目标信号稀疏与辅助交互噪声问题依然存在。其次，现有利用自监督学习（SSL）应对数据稀疏性的方法忽略了SSL任务与目标任务之间的优化失衡问题。为此，我们提出多行为自监督学习（MBSSL）框架及配套的自适应优化方法。具体而言，我们设计了一种融合自注意力机制的行为感知图神经网络，以捕捉行为的多样性与依赖关系。为增强对目标行为数据稀疏性和辅助行为噪声交互的鲁棒性，我们提出一种新型自监督学习范式，在行为间与行为内两个维度实现节点自判别。此外，我们开发了基于梯度混合操作的定制化优化策略，自适应平衡自监督学习任务与主监督推荐任务。在五个真实数据集上的大量实验表明，MBSSL相较于十种最先进（SOTA）基线方法取得了持续改进。模型实现已开源至：https://github.com/Scofield666/MBSSL.git。