Graph collaborative filtering, which learns user and item representations through message propagation over the user-item interaction graph, has been shown to effectively enhance recommendation performance. However, most current graph collaborative filtering models mainly construct the interaction graph on a single behavior domain (e.g. click), even though users exhibit various types of behaviors on real-world platforms, including actions like click, cart, and purchase. Furthermore, due to variations in user engagement, there exists an imbalance in the scale of different types of behaviors. For instance, users may click and view multiple items but only make selective purchases from a small subset of them. How to alleviate the behavior imbalance problem and utilize information from the multiple behavior graphs concurrently to improve the target behavior conversion (e.g. purchase) remains underexplored. To this end, we propose IMGCF, a simple but effective model to alleviate behavior data imbalance for multi-behavior graph collaborative filtering. Specifically, IMGCF utilizes a multi-task learning framework for collaborative filtering on multi-behavior graphs. Then, to mitigate the data imbalance issue, IMGCF improves representation learning on the sparse behavior by leveraging representations learned from the behavior domain with abundant data volumes. Experiments on two widely-used multi-behavior datasets demonstrate the effectiveness of IMGCF.

翻译：图协同过滤通过用户-物品交互图上的消息传播学习用户与物品表示，已被证明能有效提升推荐性能。然而，当前多数图协同过滤模型主要基于单一行为域（如点击）构建交互图，而实际平台中用户行为呈现多样性，包括点击、加购、购买等动作。此外，由于用户参与度的差异，不同类型行为的规模存在不平衡。例如，用户可能点击浏览多个物品，但仅从中选择性地购买少量物品。如何缓解行为不平衡问题，并同时利用多行为图的信息提升目标行为转化（如购买）仍是一个未充分探索的课题。为此，我们提出IMGCF——一个简单但有效的模型，用于缓解多行为图协同过滤中的行为数据不平衡问题。具体而言，IMGCF采用多任务学习框架实现多行为图上的协同过滤。进一步，为缓解数据不平衡问题，IMGCF通过利用数据量充足的行为域学得的表示来增强稀疏行为的表示学习。在两个广泛使用的多行为数据集上的实验证明了IMGCF的有效性。