Cross-Domain Knowledge Sharing

Updated 12 December 2025

Cross-domain knowledge sharing is the process of transferring, aligning, and fusing information between distinct domains to mitigate distribution shifts and schema heterogeneity, as seen in recommender systems and multimodal retrieval.
Modern frameworks implement specialized architectures such as two-stage pretraining–fine-tuning pipelines, mixture-of-experts, and graph-based alignment to extract both shared and domain-specific representations under privacy and non-overlap constraints.
Empirical studies demonstrate significant gains in metrics like GAUC, NDCG, and CTR, underscoring the practical benefits of robust cross-domain transfer in handling diverse and imbalanced data scenarios.

Cross-domain knowledge sharing denotes the transfer, alignment, or fusion of information, representations, and learned patterns between distinct domains, modalities, or entity types to improve data-driven inference or decision-making in a target domain. This paradigm has evolved to solve the challenges of distributional divergence, schema heterogeneity, and knowledge fragmentation that inhibit generalization and sample efficiency across tasks in diverse fields—from recommender systems and AI-driven knowledge discovery, to unsupervised domain adaptation and multimodal retrieval. Modern frameworks leverage architectures, loss functions, and alignment strategies specifically tailored to the discrepancies and complementarity between domains, sometimes under privacy and non-overlap constraints.

1. Theoretical Foundations and Motivations

Cross-domain knowledge sharing formalizes knowledge transfer in settings where the source and target domains possess different data distributions, feature schemas, or semantic structures. It distinguishes itself from classical transfer learning by explicitly confronting:

Heterogeneity in feature spaces, where certain attributes are missing or carry distinct meaning per domain (e.g., product price vs. text post content) (Guan et al., 29 Feb 2024).
Distributional shift, with divergent or long-tailed population statistics (e.g., cold-start domains, imbalanced item popularity) (Yang et al., 25 Jun 2024, Ahangama et al., 2019).
Lack of direct sample correspondence, such as non-overlapping user populations in federated recommender systems (Liu et al., 17 Mar 2025).
Different label or task spaces, especially in fully heterogeneous knowledge transfer (Huo et al., 8 Dec 2025, Chen et al., 2023).

Foundational results in unsupervised domain adaptation (UDA) establish the Ben-David bound, decomposing target-domain generalization error as the sum of source error, distribution discrepancy, and a shared expected loss capturing intrinsic labeling disagreement (Chen et al., 2023). This underscores that naive global alignment is both insufficient and potentially detrimental when domain-specific semantics or support regions diverge.

2. Architectural Approaches and Knowledge Bridges

Cross-domain sharing mechanisms are primarily structured via explicitly designed modules and architectural pipelines. Prominent strategies include:

Two-stage pretraining–fine-tuning pipelines: As in the Multi-entity Knowledge Transfer (MKT) framework, a shared multi-entity model is first pretrained on both source and target domains with feature alignment and knowledge extraction (common and specific), then knowledge is gated into a task-specific fine-tuned model (Guan et al., 29 Feb 2024).
Mixture-of-experts federated architectures: FMoE-CDSR treats each domain’s model as a “frozen expert,” importing others’ parameters and gating their contributions via a learnable mechanism, with no sharing of raw data or user embeddings, addressing the non-overlap and privacy constraints in federated settings (Liu et al., 17 Mar 2025).
Graph-based alignment and imputation: Graph-enabled frameworks construct affinity graphs over source–target entities, then propagate label or embedding information via graph diffusion or graph neural networks (GNNs) to align and complete representations (Yao, 2023, Shen, 2019).
Shared and domain-specific module design: CoNet augments dual feedforward networks with full-matrix cross-connections, enabling adaptive transfer of latent features at every hidden layer; sparse penalties allow selective transfer (Hu et al., 2018). Transformer-based UDA models inject separate classification tokens with masked self-attention to disentangle domain-invariant from domain-specific knowledge (Ma et al., 2021).
Multi-agent and multi-modal orchestrations: Systems such as MetaGPT coordinate domain-specialized agents, each manipulating its own knowledge base, to collaboratively solve interdisciplinary queries beyond the reach of a single model. Information is fused through context-passing and shared attention spaces (Aryal et al., 12 Apr 2024).
Knowledge graph-based item linkage: Cross-domain recommendations aligned via joint KG representations, with mutual information maximization and factorization, capturing both domain-specific and general semantics (Zhang et al., 2022, Ma et al., 2020).
Contrastive learning for cross-manifold alignment: Hyperbolic CDR frameworks embed users/items on separate hyperbolic manifolds, then bridge domains with differential geometry-based mappings plus cross-domain contrastive objectives (Yang et al., 25 Jun 2024).

The following table summarizes representative architectural motifs:

Framework/Class	Shared Module	Bridge Mechanism	Domain Handling
MKT (Guan et al., 29 Feb 2024)	HFA, CKE	Gated plug-in	Multi-entity structs
FMoE-CDSR (Liu et al., 17 Mar 2025)	Adapter, gate net	MoE w/ frozen experts	Privacy, non-overlap
CoNet (Hu et al., 2018)	Dual FFNNs, X-conn.	Layerwise transfer	Overlapped user/item
MetaGPT (Aryal et al., 12 Apr 2024)	N/A (agents)	Orchestration context	Multi-domain agents
HG-constrastive (Yang et al., 25 Jun 2024)	Hyperbolic embeddings	Map + contrastive	Curvature-per-domain

3. Feature Alignment, Knowledge Extraction, and Representation Matching

Robust cross-domain transfer demands precise alignment and extraction of both shared and domain-specific knowledge, often requiring sophisticated pre-processing and loss design:

Feature Alignment Modules (HFA): Scale and re-weight heterogeneous feature schemas into a common vector space, enabling subsequent knowledge extraction (Guan et al., 29 Feb 2024).
Domain- and entity-specific extractors: Extract domain-invariant (“common”) and domain-specific (“individual”) factors via shared-private network heads (e.g., PLE-style towers, Tree-LSTM encoders for NLG) (Guan et al., 29 Feb 2024, Tseng et al., 2019).
Polarized/orthogonal objectives: Prevent negative transfer by imposing loss terms that decorrelate or orthogonalize common and specific representations (polarized distribution loss (Guan et al., 29 Feb 2024); stop-gradient constraints in UDA transformers (Ma et al., 2021)).
Graph and manifold alignment: Diffusion-based imputation on k-NN or MST graphs ensures smooth propagation of high-quality representations; learnable manifold mappings align cross-curvature embeddings (Yao, 2023, Yang et al., 25 Jun 2024).
Semantic subdomain partitioning: Hierarchical or knowledge-inspired subdomaining divides each domain into coherent subregions (by card type, time, spatial grid, etc.), matches them by class-wise distance, and aligns only the most compatible pairs, minimizing shared expected loss (Chen et al., 2023).
Contrastive learning and mutual information maximization: Alignment between source and target is enforced by InfoNCE or other contrastive losses, or by maximizing the MI between graph-based and interaction-based representations (Zhang et al., 2022, Yang et al., 25 Jun 2024, Wen et al., 2022).

Knowledge sharing modalities are dictated by the context and constraints of the application domain:

Direct parameter or factor sharing: Latent factor models may tie overlapping users’ embeddings across domains via consensus optimization (as in CDIMF, utilizing ADMM for cross-domain matrix factorization (Samra et al., 23 Sep 2024)); dual-domain neural or VAE architectures concatenate representations with necessary mapping for cold-start or sparse scenarios (Ahangama et al., 2019).
Cross-domain sequence and multimodal fusion: Mixture-of-experts (MoE) gates, GNNs, and graph convolutional mechanisms adaptively select or fuse behavioral and knowledge pathways—the MIFN combines behavioral sequence transfer with multi-hop knowledge graph propagation, using a dynamic mode switch (Ma et al., 2020).
Agent- and workflow-based synthesis: Task-specific or workflow-oriented collaboration, such as routing interdisciplinary queries through specialized agents with shared vector spaces, facilitates synthesis of disparate information (Aryal et al., 12 Apr 2024).
Graph-based and semantic path querying: Predicate-level fuzzy clustering and automated ontology mapping organize big linked data for efficient cross-domain SPARQL querying, extracting composite knowledge evidence across medical or scientific domains (Shen, 2019).
Explicit connectivity augmentation (superhighway construction): Graph connectivity is enhanced by adding direct, high-weighted edges between cross-domain users with sufficient shared-item activity, augmenting standard CF methods without altering raw data (Lai et al., 2018).

5. Empirical Results and Performance Characteristics

Benchmarks across domains consistently reveal sizable gains from cross-domain knowledge sharing when alignment quality is controlled:

Multi-entity CDR: MKT achieves a significant GAUC gain (+0.6%) over strong fine-tuning baselines and produces an online CTR increase of 4.13%, with largest benefits for cold-start users (Guan et al., 29 Feb 2024).
Federated non-overlapped CDR: FMoE-CDSR realizes up to +22% improvement in NDCG and HR on Amazon review splits, with robust privacy due to frozen expert exchange (Liu et al., 17 Mar 2025).
KG-informed and graph-enabled methods: KG-NeuCMF outperforms baselines by 5–12% HR/NDCG, with MI and alignment terms essential for “cold” item generalization; graph-enabled imputation achieves 70–80% accuracy in embedding recovery and 8–10% perplexity reduction downstream (Zhang et al., 2022, Yao, 2023).
Domain adaptation and UDA: WinTR surpasses prior state-of-the-art by 2–5 points on Office-Home and VisDA benchmarks, with ablation underscoring the necessity of separate domain tokens and single-sided alignment (Ma et al., 2021). KISA improves up to +4.8% AUC in cross-border fraud detection and achieves subdomain-level RMSE gains in cross-city time-series forecasting (Chen et al., 2023).
Recommendation and retrieval frameworks: Superhighway construction adds >20 MAP points to pure collaborative filtering on both source and target domains (Lai et al., 2018), while graph-enabled 3D shape retrieval achieves mAP ≈ 93% and superior performance in cross-modal scenarios (Chang et al., 2022).

6. Limitations, Open Challenges, and Future Directions

Several structural and theoretical limitations persist:

Negative transfer remains a risk, especially if feature or semantic overlaps are weak or non-existent. Polarized or adversarial alignment may not suffice in fully heterogeneous settings (Guan et al., 29 Feb 2024, Huo et al., 8 Dec 2025, Serrano et al., 26 Apr 2024).
Scalability and coordination cost: Multi-agent orchestration, subdomain partitioning, and large-scale graph propagation introduce computational bottlenecks, requiring hierarchical or attention mechanisms for tractability (Aryal et al., 12 Apr 2024, Chen et al., 2023, Yao, 2023).
Dependency on alignment quality and data coverage: Methods are sensitive to the extent and nature of overlapping users/items, alignment mapping, and graph connectivity. Failed alignments can result in performance collapse (as in HGCF-merge and data-poor agent bootstraps) (Yang et al., 25 Jun 2024, Liu et al., 17 Mar 2025).
Continued need for theoretical guarantees: Quantifying transferable kernels, optimal source selection, and tighter bounds on negative transfer remain open, especially in RL and unsupervised regimes (Serrano et al., 26 Apr 2024, Serrano et al., 2023).
Robust privacy and data governance: Federated approaches help but do not eliminate all privacy concerns; further work is needed for automated, verifiable privacy in knowledge sharing (Liu et al., 17 Mar 2025).

Table: Open Issues and Promising Strategies

Limitation/Challenge	Promising Strategy	Paper Reference
Negative transfer	Source selection, polarized loss	(Guan et al., 29 Feb 2024, Serrano et al., 26 Apr 2024)
Alignment under divergence	Adversarial/multi-manifold mapping	(Huo et al., 8 Dec 2025, Yang et al., 25 Jun 2024)
Scalability (agent/graph)	Hierarchical orchestration, sparse	(Aryal et al., 12 Apr 2024, Yao, 2023)
Cold-start/long-tail	Hyperbolic embeddings, KG MI	(Yang et al., 25 Jun 2024, Zhang et al., 2022)
Multi-source and continuous domains	Dynamic gating, lifelong UDA	(Chen et al., 2023, Serrano et al., 26 Apr 2024)

7. Generalization Beyond Recommendation and Towards Universal Knowledge Networks

While the recommender systems literature has been a principal driver of cross-domain knowledge sharing, the methodologies extend to:

AI system orchestration for interdisciplinary scientific reasoning (multi-agent, multi-modal RAG pipelines) (Aryal et al., 12 Apr 2024).
Biomedical, financial, and web-scale knowledge integration via graph-partitioned and semantic-layered analytics (Shen, 2019, Yao, 2023).
Multimodal and cross-modal retrieval, including vision–language dual-stream encoders with cross-modal contrastive sharing (Wen et al., 2022).
Cross-domain RL, wherein agent skills, policies, or value functions are transferred via latent alignment, reward-shaping, or expert pseudo-labeling considering state–action heterogeneity (Serrano et al., 26 Apr 2024, Serrano et al., 2023).
Semantic adaptation and NLG, via hierarchical tree-structured encoders and layerwise attentional sharing (Tseng et al., 2019).

This trend suggests an ongoing convergence toward universal architectures and representation spaces capable of supporting robust generalization and knowledge propagation across increasingly diverse and complex domains.

References:

(Guan et al., 29 Feb 2024, Liu et al., 17 Mar 2025, Aryal et al., 12 Apr 2024, Hu et al., 2018, Tseng et al., 2019, Zhang et al., 2022, Ahangama et al., 2019, Ma et al., 2020, Yao, 2023, Yang et al., 25 Jun 2024, Huo et al., 8 Dec 2025, Serrano et al., 26 Apr 2024, Wen et al., 2022, Chang et al., 2022, Shen, 2019, Ma et al., 2021, Samra et al., 23 Sep 2024, Lai et al., 2018, Serrano et al., 2023, Chen et al., 2023).