  Recently, the growth of service platforms brings great convenience to both
users and merchants, where the service search engine plays a vital role in
improving the user experience by quickly obtaining desirable results via
textual queries. Unfortunately, users' uncontrollable search customs usually
bring vast amounts of long-tail queries, which severely threaten the capability
of search models. Inspired by recently emerging graph neural networks (GNNs)
and contrastive learning (CL), several efforts have been made in alleviating
the long-tail issue and achieve considerable performance. Nevertheless, they
still face a few major weaknesses. Most importantly, they do not explicitly
utilize the contextual structure between heads and tails for effective
knowledge transfer, and intention-level information is commonly ignored for
more generalized representations.
  To this end, we develop a novel framework GARCIA, which exploits the graph
based knowledge transfer and intention based representation generalization in a
contrastive setting. In particular, we employ an adaptive encoder to produce
informative representations for queries and services, as well as hierarchical
structure aware representations of intentions. To fully understand tail queries
and services, we equip GARCIA with a novel multi-granularity contrastive
learning module, which powers representations through knowledge transfer,
structure enhancement and intention generalization. Subsequently, the complete
GARCIA is well trained in a pre-training&fine-tuning manner. At last, we
conduct extensive experiments on both offline and online environments, which
demonstrates the superior capability of GARCIA in improving tail queries and
overall performance in service search scenarios.
