  In this paper, we propose a novel model named DemiNet (short for
DEpendency-Aware Multi-Interest Network) to address the above two issues. To be
specific, we first consider various dependency types between item nodes and
perform dependency-aware heterogeneous attention for denoising and obtaining
accurate sequence item representations. Secondly, for multiple interests
extraction, multi-head attention is conducted on top of the graph embedding. To
filter out noisy inter-item correlations and enhance the robustness of
extracted interests, self-supervised interest learning is introduced to the
above two steps. Thirdly, to aggregate the multiple interests, interest experts
corresponding to different interest routes give rating scores respectively,
while a specialized network assigns the confidence of each score. Experimental
results on three real-world datasets demonstrate that the proposed DemiNet
significantly improves the overall recommendation performance over several
state-of-the-art baselines. Further studies verify the efficacy and
interpretability benefits brought by the fine-grained user interest modeling.
