In the one-class recommendation problem, it's required to make recommendations basing on users' implicit feedback, which is inferred from their action and inaction. Existing works obtain representations of users and items by encoding positive and negative interactions observed from training data. However, these efforts assume that all positive signals from implicit feedback reflect a fixed preference intensity, which is not realistic. Consequently, representations learned with these methods usually fail to capture informative entity features that reflect various preference intensities. In this paper, we propose a multi-tasking framework taking various preference intensities of each signal from implicit feedback into consideration. Representations of entities are required to satisfy the objective of each subtask simultaneously, making them more robust and generalizable. Furthermore, we incorporate attentive graph convolutional layers to explore high-order relationships in the user-item bipartite graph and dynamically capture the latent tendencies of users toward the items they interact with. Experimental results show that our method performs better than state-of-the-art methods by a large margin on three large-scale real-world benchmark datasets.

翻译：在单类推荐问题中，需要基于用户隐式反馈（即从其行为与非行为推断）进行推荐。现有工作通过编码训练数据中观察到的正负交互，学习用户和项目的表示。然而，这些方法假设隐式反馈中的所有正信号反映相同的偏好强度，这并不符合实际情况。因此，通过这些方法学习到的表示通常难以捕捉反映不同偏好强度的信息性实体特征。本文提出一个考虑隐式反馈中每个信号的不同偏好强度的多任务框架。该框架要求实体表示同时满足每个子任务的目标，使其更具鲁棒性和泛化能力。此外，我们引入注意力图卷积层，以探索用户-项目二部图中的高阶关系，并动态捕捉用户对其交互项目的潜在倾向。实验结果表明，在三个大规模真实世界基准数据集上，我们的方法显著优于现有最先进方法。