  Researchers have begun to utilize heterogeneous knowledge graphs (KGs) as
auxiliary information in recommendation systems to mitigate the cold start and
sparsity issues. However, utilizing a graph neural network (GNN) to capture
information in KG and further apply in RS is still problematic as it is unable
to see each item's properties from multiple perspectives. To address these
issues, we propose the multi-view item network (MVIN), a GNN-based
recommendation model which provides superior recommendations by describing
items from a unique mixed view from user and entity angles. MVIN learns item
representations from both the user view and the entity view. From the user
view, user-oriented modules score and aggregate features to make
recommendations from a personalized perspective constructed according to KG
entities which incorporates user click information. From the entity view, the
mixing layer contrasts layer-wise GCN information to further obtain
comprehensive features from internal entity-entity interactions in the KG. We
evaluate MVIN on three real-world datasets: MovieLens-1M (ML-1M), LFM-1b 2015
(LFM-1b), and Amazon-Book (AZ-book). Results show that MVIN significantly
outperforms state-of-the-art methods on these three datasets. In addition, from
user-view cases, we find that MVIN indeed captures entities that attract users.
Figures further illustrate that mixing layers in a heterogeneous KG plays a
vital role in neighborhood information aggregation.
