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.

翻译：为帮助商家/客户通过小程序提供/获取多样化服务，在线服务平台在高效内容分发中占据关键地位，其中如何为顾客推荐服务提供商新推出的领域内项目已成为亟待解决的问题。然而，源领域与多元化目标领域间不可忽视的差异对跨领域推荐系统构成了重大挑战，这在工业场景中常导致性能瓶颈。尽管实体图谱具有作为领域间桥梁的潜力，但粗浅的利用方式仍难以提炼有用知识，甚至引发负迁移问题。为此，我们提出PEACE——一种面向跨领域推荐的原型学习增强可迁移框架。在弥合领域差异方面，PEACE基于多兴趣与实体导向的预训练架构构建，该架构不仅能以多粒度方式促进通用知识的学习，还能有效利用实体图谱中的结构化信息。进而，我们将原型学习引入源领域的预训练过程，通过对比原型学习模块和原型增强注意力机制实现自适应知识利用，大幅提升用户与项目的表示质量。为缓解在线服务的压力，PEACE采用轻量化部署方案，并在在线与离线环境中均观察到显著的性能提升。