Multi-types of user behavior data (e.g., clicking, adding to cart, and purchasing) are recorded in most real-world recommendation scenarios, which can help to learn users' multi-faceted preferences. However, it is challenging to explore multi-behavior data due to the unbalanced data distribution and sparse target behavior, which lead to the inadequate modeling of high-order relations when treating multi-behavior data ''as features'' and gradient conflict in multitask learning when treating multi-behavior data ''as labels''. In this paper, we propose CIGF, a Compressed Interaction Graph based Framework, to overcome the above limitations. Specifically, we design a novel Compressed Interaction Graph Convolution Network (CIGCN) to model instance-level high-order relations explicitly. To alleviate the potential gradient conflict when treating multi-behavior data ''as labels'', we propose a Multi-Expert with Separate Input (MESI) network with separate input on the top of CIGCN for multi-task learning. Comprehensive experiments on three large-scale real-world datasets demonstrate the superiority of CIGF. Ablation studies and in-depth analysis further validate the effectiveness of our proposed model in capturing high-order relations and alleviating gradient conflict. The source code and datasets are available at https://github.com/MC-CV/CIGF.

翻译：现实推荐场景中记录了多种用户行为数据（如点击、加入购物车和购买），这有助于学习用户的多方面偏好。然而，由于数据分布不均衡和目标行为稀疏，多行为数据的探索面临挑战：将多行为数据视为"特征"时会导致高阶关系建模不充分，而将其视为"标签"时则在多任务学习中引发梯度冲突。本文提出一种基于压缩交互图的框架CIGF以克服上述局限。具体而言，我们设计了一种新颖的压缩交互图卷积网络（CIGCN）来显式建模实例级高阶关系。为缓解将多行为数据视为"标签"时可能产生的梯度冲突，我们在CIGCN基础上提出具有独立输入的多专家网络（MESI）用于多任务学习。在三个大规模真实数据集上的综合实验证明了CIGF的优越性。消融实验和深入分析进一步验证了所提模型在捕获高阶关系和缓解梯度冲突方面的有效性。源代码和数据集请见 https://github.com/MC-CV/CIGF。