  Heterogeneous information network has been widely used to alleviate sparsity
and cold start problems in recommender systems since it can model rich context
information in user-item interactions. Graph neural network is able to encode
this rich context information through propagation on the graph. However,
existing heterogeneous graph neural networks neglect entanglement of the latent
factors stemming from different aspects. Moreover, meta paths in existing
approaches are simplified as connecting paths or side information between node
pairs, overlooking the rich semantic information in the paths. In this paper,
we propose a novel disentangled heterogeneous graph attention network DisenHAN
for top-$N$ recommendation, which learns disentangled user/item representations
from different aspects in a heterogeneous information network. In particular,
we use meta relations to decompose high-order connectivity between node pairs
and propose a disentangled embedding propagation layer which can iteratively
identify the major aspect of meta relations. Our model aggregates corresponding
aspect features from each meta relation for the target user/item. With
different layers of embedding propagation, DisenHAN is able to explicitly
capture the collaborative filtering effect semantically. Extensive experiments
on three real-world datasets show that DisenHAN consistently outperforms
state-of-the-art approaches. We further demonstrate the effectiveness and
interpretability of the learned disentangled representations via insightful
case studies and visualization.
