  Cold-start rating prediction is a fundamental problem in recommender systems
that has been extensively studied. Many methods have been proposed that exploit
explicit relations among existing data, such as collaborative filtering, social
recommendations and heterogeneous information network, to alleviate the data
insufficiency issue for cold-start users and items. However, the explicit
relations constructed based on data between different roles may be unreliable
and irrelevant, which limits the performance ceiling of the specific
recommendation task. Motivated by this, in this paper, we propose a flexible
framework dubbed heterogeneous interaction rating network (HIRE). HIRE dose not
solely rely on the pre-defined interaction pattern or the manually constructed
heterogeneous information network. Instead, we devise a Heterogeneous
Interaction Module (HIM) to jointly model the heterogeneous interactions and
directly infer the important interactions via the observed data. In the
experiments, we evaluate our model under three cold-start settings on three
real-world datasets. The experimental results show that HIRE outperforms other
baselines by a large margin. Furthermore, we visualize the inferred
interactions of HIRE to confirm the contribution of our model.
