# Breaking the Information Silo: Semantic Personas for Cross-Domain Recommendation

arXiv: https://arxiv.org/abs/2606.01783 · HTML: https://mlsanity.com/paper/2606.01783/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 1 Jun 2026.

## Builds on (most cited first, 20 of 20)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings](https://mlsanity.com/paper/1909.00512/) (2019)
- [Text and Code Embeddings by Contrastive Pre-Training](https://mlsanity.com/paper/2201.10005/) (2022)
- [Matryoshka Representation Learning](https://mlsanity.com/paper/2205.13147/) (2022)
- [User-LLM: Efficient LLM Contextualization with User Embeddings](https://mlsanity.com/paper/2402.13598/) (2024)
- [Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering](https://mlsanity.com/paper/2009.03376/) (2020)
- [A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions](https://mlsanity.com/paper/2108.03357/) (2021)
- [Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions](https://mlsanity.com/paper/2311.04590/) (2023)
- [One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems](https://mlsanity.com/paper/2310.14304/) (2023)
- [Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2504.18383/) (2025)
- [Understanding Before Recommendation: Semantic Aspect-Aware Review Exploitation via Large Language Models](https://mlsanity.com/paper/2312.16275/) (2023)
- [A Pre-trained Sequential Recommendation Framework: Popularity Dynamics for Zero-shot Transfer](https://mlsanity.com/paper/2401.01497/) (2024)
- [Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation](https://mlsanity.com/paper/2401.14678/) (2024)
- [You Are What You Bought: Generating Customer Personas for E-commerce Applications](https://mlsanity.com/paper/2504.17304/) (2025)
- [Uncovering Cross-Domain Recommendation Ability of Large Language Models](https://mlsanity.com/paper/2503.07761/) (2025)
- [Federated Semantic Learning for Privacy-preserving Cross-domain Recommendation](https://mlsanity.com/paper/2503.23026/) (2025)
- [X-Cross: Dynamic Integration of Language Models for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2504.20859/) (2025)
- [Cross-domain Recommender Systems via Multimodal Domain Adaptation](https://mlsanity.com/paper/2306.13887/) (2023)
- [Cross-domain Transfer of Valence Preferences via a Meta-optimization Approach](https://mlsanity.com/paper/2406.16494/) (2024)
- [FedCRF: A Federated Cross-domain Recommendation Method with Semantic-driven Deep Knowledge Fusion](https://mlsanity.com/paper/2604.17681/) (2026)

## Abstract (arXiv)

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.