Digital platforms increasingly operate as isolated information silos, limiting their ability to construct comprehensive user representations across domains. Cross-domain recommender systems seek to overcome this limitation by transferring knowledge from a source domain to a target domain, yet most existing approaches depend on shared users, shared items, or structurally similar interaction graphs. These assumptions are often unrealistic across independent platforms. We propose SPHERE (Semantic Personas for Heterogeneous cross-domain Recommendation), a design artifact that enables recommendation knowledge transfer across strictly disjoint domains with no shared users or items. Rather than aligning domains through identity or graph structure, SPHERE uses large language models to induce a shared behavioral vocabulary, generate structured semantic personas for users, and retrieve behaviorally similar source-domain communities that form a Community Source Persona. This semantic signal is integrated with collaborative signals through a dual-tower architecture and dynamic fusion gate, allowing SPHERE to augment standard recommender backbones. Empirical evaluation across Amazon Books, Goodreads, and Steam demonstrates consistent improvements over NCF, SVD++, and LightGCN baselines under full-ranking evaluation. The results show that cross-domain transfer effectiveness is not determined solely by semantic proximity between domains; rather, it depends critically on the structural density and native predictive strength of the target domain. The study contributes to information systems research by reframing cross-domain personalization as behavior-based semantic alignment, offering a practical mechanism for overcoming information silos while preserving interpretability and modularity.

翻译：数字平台日益成为孤立的信息孤岛，限制了其跨域构建全面用户表征的能力。跨域推荐系统试图通过将知识从源域迁移至目标域来克服这一局限，但现有方法大多依赖共享用户、共享物品或结构相似的交互图。这些假设在独立平台间往往不切实际。我们提出SPHERE（面向异构跨域推荐的语义角色画像），这是一种设计构件，能够在无共享用户或物品的严格独立域间实现推荐知识迁移。SPHERE不通过身份或图结构对齐域，而是利用大语言模型生成共享行为词汇表，为用户构建结构化语义角色画像，并检索行为相似的源域社区以形成社区源角色画像。该语义信号通过双塔架构与动态融合门与协同信号集成，使SPHERE能够增强标准推荐主干网络。在亚马逊图书、Goodreads和Steam平台上的实证评估表明，在全排序评估下，SPHERE相比NCF、SVD++和LightGCN基线模型取得了一致性改进。结果显示，跨域迁移效果并非仅由域间语义接近度决定，而是关键取决于目标域的结构密度与原生预测强度。本研究将跨域个性化重新定位为基于行为的语义对齐，为信息系统研究提供了以可解释性与模块化方式克服信息孤岛的实践机制。