CADKR: A Context-Aware Dialog-Based Knowledge Recommendation Model for Industrial Software Systems

Industrial software systems underpin complex industrial platforms by integrating cross-disciplinary expertize and sophisticated workflows. Their inherent complexity generates substantial cognitive demands, necessitating contextual, adaptive, and personalized knowledge support aligned with dynamic tasks and evolving expertize. Conventional recommendation approaches struggle to address heterogeneous knowledge sources, dynamic user needs with intent drift, and the domain-specific semantics required in industrial software. To overcome these challenges, we propose a context-aware dialog-based knowledge recommendation (CADKR) model, powered by large language models (LLMs). CADKR fuses dynamic interaction context with domain knowledge through a novel recommendation network (RecNet), enabling robust generalization to unseen scenarios and adaptability to preference drift. For practical deployment, lightweight optimization strategies compress model size by up to 98% without compromising accuracy. A case study in a large chemical industrial park demonstrates the effectiveness of CADKR in enhancing industrial knowledge support, while application deployments in leading new energy vehicle enterprises and the world’s largest circular resource power plant have yielded an independent verification report, providing strong evidence of the proposed method’s practical value.11The report is available athttps://anonymous.4open.science/r/verification-report. Code and data are available athttps://anonymous.4open.science/r/CADKR-TCSS. To meet double-blind requirements, organization and personnel details are anonymized and will be disclosed after acceptance.