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.

在这篇论文中，我们提出了一个多任务框架，考虑到隐式反馈中每个信号的各种偏好强度，要求实体的表示同时满足每个子任务的目标，使其更具鲁棒性和可推广性。此外，我们还结合了注意力图卷积层，以探索用户 - 物品双向图中的高阶关系，并动态捕捉用户对其交互物品的潜在倾向。实验结果表明，我们的方法在三个大规模真实世界基准数据集上比现有最先进方法有显著优势。