Breaking the Information Silo: Semantic Personas for Cross-Domain Recommendation
2026-06-01 • Information Retrieval
Information RetrievalArtificial Intelligence
AI summaryⓘ
The authors address the problem that digital platforms often work separately, making it hard to recommend things across different sites because they don’t share user or item info. They created SPHERE, a method that uses large language models to find shared behavior patterns (semantic personas) instead of relying on shared users or items. This helps recommenders learn from one domain to improve recommendations in another, even when the platforms are completely separate. Their tests on Amazon Books, Goodreads, and Steam showed SPHERE improves recommendation quality, especially when the target domain is well-structured and predictive.
cross-domain recommendationlarge language modelssemantic personascollaborative filteringdual-tower architectureinformation silosbehavioral alignmentrecommendation systemscommunity source personadynamic fusion gate
Authors
Jonathan Mayo, Moshe Unger, Konstantin Bauman
Abstract
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.