  Online service platforms offering a wide range of services through miniapps
have become crucial for users who visit these platforms with clear intentions
to find services they are interested in. Aiming at effective content delivery,
cross-domain recommendation are introduced to learn high-quality
representations by transferring behaviors from data-rich scenarios. However,
these methods overlook the impact of the decision path that users take when
conduct behaviors, that is, users ultimately exhibit different behaviors based
on various intents. To this end, we propose HIER, a novel Hierarchical decIsion
path Enhanced Representation learning for cross-domain recommendation. With the
help of graph neural networks for high-order topological information of the
knowledge graph between multi-source behaviors, we further adaptively learn
decision paths through well-designed exemplar-level and information bottleneck
based contrastive learning. Extensive experiments in online and offline
environments show the superiority of HIER.
