Neural Feature Crossing for Cross-Domain Recommendation in Knowledge Management Systems
Year: 2026
Type: article
Abstract: Recently, the rapid growth of digital knowledge resources has created an urgent need for advanced recommendation systems that capable to operate across various domains. This paper introduces KnowledgeXRec, a domain-aware hybrid recommendation framework that models both shared and domain-specific user-item interactions within a unified end-to-end architecture. Domain-specific feature crosses capture patterns unique to educational and cultural domains, while shared layers enable cross-domain knowledge transfer. Learnable embedding is represented categorical features such as learning style, age group, and content attributes, and the model is jointly optimized using binary cross-entropy loss with Adam optimizer. Findings provide evidence that KnowledgeXRec consistently outperforms seven state-of-the-art baselines on the OULAD (educational) and Goodreads (cultural) datasets. The model achieves enhancement of up to 15.6% in accuracy, 18.3% in diversity, and 54.8% in novelty metrics, and statistical analysis indicates the significance of these gains (p < 0.01). Overall, KnowledgeXRec offers a scalable and adaptable architecture for intelligent knowledge management systems, which enable effective cross-domain recommendations while maintain a strong domain-specific performance. This study illustrates that combining neural feature interactions with domain adaptation strategies introduce challenges in knowledge-intensive recommendation scenarios, such as cold-start problems, data sparsity, and heterogeneous domains. A new direction for future work is to extend the framework to multi-modal data, temporal interactions, and privacy-preserving federated learning, can further improve its applicability in diverse real-world knowledge management environments.
Source: Baghdad Science Journal
Authors: Imran Khaled, Faiez Gargouri
Institution: University of Sfax
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $150
OpenAlex ID: W7166309828
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