  In the past decade, the heterogeneous information network (HIN) has become an
important methodology for modern recommender systems. To fully leverage its
power, manually designed network templates, i.e., meta-structures, are
introduced to filter out semantic-aware information. The hand-crafted
meta-structure rely on intense expert knowledge, which is both laborious and
data-dependent. On the other hand, the number of meta-structures grows
exponentially with its size and the number of node types, which prohibits
brute-force search. To address these challenges, we propose Genetic
Meta-Structure Search (GEMS) to automatically optimize meta-structure designs
for recommendation on HINs. Specifically, GEMS adopts a parallel genetic
algorithm to search meaningful meta-structures for recommendation, and designs
dedicated rules and a meta-structure predictor to efficiently explore the
search space. Finally, we propose an attention based multi-view graph
convolutional network module to dynamically fuse information from different
meta-structures. Extensive experiments on three real-world datasets suggest the
effectiveness of GEMS, which consistently outperforms all baseline methods in
HIN recommendation. Compared with simplified GEMS which utilizes hand-crafted
meta-paths, GEMS achieves over $6\%$ performance gain on most evaluation
metrics. More importantly, we conduct an in-depth analysis on the identified
meta-structures, which sheds light on the HIN based recommender system design.
