Interactive recommendation algorithm based on intelligent assistance for project application

To address the critical issues in project application management, such as inaccurate and inadequate understanding of guidelines, non-standardized preparation of application materials, and time-consuming formal reviews, this paper proposes a Knowledge Graph-Enhanced Interactive Recommendation Intelligent Assistant Model for Project Applications. Different from existing LLM-based assistants that suffer from severe domain knowledge hallucinations and lack long-term decision optimization capabilities, and knowledge graph/RL-based systems that lack natural language interaction capabilities, this model innovatively integrates the natural language interaction advantages of Large Language Models (LLMs) with the precise decision-making strengths of knowledge graph-enhanced reinforcement learning (RL), and is the first to achieve the three-in-one integration of three core technologies: (1) A domain-specific knowledge graph (PAG-KG) for project applications, which constructs a multi-entity and multi-relation structured knowledge base to provide reliable domain prior knowledge and fundamentally mitigate the knowledge hallucination problem of LLMs; (2) An LLM decision brain equipped with plan-first execution and Actor-Critic reflection mechanisms, which supports natural language interaction and orchestrates a suite of intelligent assistant tools covering core functions including in-depth guideline comprehension and formal review of application materials to efficiently complete various tasks; (3) A knowledge graph-enhanced Dueling DQN (KG-DQN) tailored for long-term multi-turn interaction scenarios, which dynamically optimizes interactive decision-making strategies. To comprehensively validate the performance of the proposed model, a dedicated dataset for science and technology project applications was constructed, and multi-dimensional experimental validations were conducted. The results demonstrate that the proposed model significantly outperforms baseline models in key metrics such as guideline understanding accuracy, material preparation completeness, and the precision and recall rates of formal reviews, exhibits excellent adaptability in complex multi-turn interactions and strong generalization capabilities, can substantially reduce application time and manual intervention, and effectively enhances the efficiency and intelligence level of project application management. Notably, this three-in-one technical integration enables full-process intelligent assistance for project applications, a breakthrough achievement that has not been realized in previous studies.