  To help merchants/customers to provide/access a variety of services through
miniapps, online service platforms have occupied a critical position in the
effective content delivery, in which how to recommend items in the new domain
launched by the service provider for customers has become more urgent. However,
the non-negligible gap between the source and diversified target domains poses
a considerable challenge to cross-domain recommendation systems, which often
leads to performance bottlenecks in industrial settings. While entity graphs
have the potential to serve as a bridge between domains, rudimentary
utilization still fail to distill useful knowledge and even induce the negative
transfer issue. To this end, we propose PEACE, a Prototype lEarning Augmented
transferable framework for Cross-domain rEcommendation. For domain gap
bridging, PEACE is built upon a multi-interest and entity-oriented pre-training
architecture which could not only benefit the learning of generalized knowledge
in a multi-granularity manner, but also help leverage more structural
information in the entity graph. Then, we bring the prototype learning into the
pre-training over source domains, so that representations of users and items
are greatly improved by the contrastive prototype learning module and the
prototype enhanced attention mechanism for adaptive knowledge utilization. To
ease the pressure of online serving, PEACE is carefully deployed in a
lightweight manner, and significant performance improvements are observed in
both online and offline environments.
