  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.
