  Heterogeneous Information Networks (HINs) are labeled graphs that depict
relationships among different types of entities (e.g., users, movies and
directors). For HINs, meta-path-based recommenders (MPRs) utilize meta-paths
(i.e., abstract paths consisting of node and link types) to predict user
preference, and have attracted a lot of attention due to their explainability
and performance. We observe that the performance of MPRs is highly sensitive to
the meta-paths they use, but existing works manually select the meta-paths from
many possible ones. Thus, to discover effective meta-paths automatically, we
propose the Reinforcement learning-based Meta-path Selection (RMS) framework.
Specifically, we define a vector encoding for meta-paths and design a policy
network to extend meta-paths. The policy network is trained based on the
results of downstream recommendation tasks and an early stopping approximation
strategy is proposed to speed up training. RMS is a general model, and it can
work with all existing MPRs. We also propose a new MPR called RMS-HRec, which
uses an attention mechanism to aggregate information from the meta-paths. We
conduct extensive experiments on real datasets. Compared with the manually
selected meta-paths, the meta-paths identified by RMS consistently improve
recommendation quality. Moreover, RMS-HRec outperforms state-of-the-art
recommender systems by an average of 7% in hit ratio. The codes and datasets
are available on https://github.com/Stevenn9981/RMS-HRec.
