EchoKG: A Dynamic user Preference Knowledge Graph In-vehicle Dialogue System Based on Ebbinghaus Forgetting Curve - Liang, Yuqian (International Journal of Advanced AI Applications, 2026)
With the increasing integration of large language models (LLMs) into intelligent vehicle cockpits, achieving efficient, accurate, and personalized interactions with long-term memory capabilities has become a key challenge. Existing vector retrieval methods suffer from context inflation issues, while static knowledge graphs struggle to capture the time-varying nature of user preferences. This paper proposes the EchoKG framework, which for the first time mathematically models the Ebbinghaus forgetting curve as a dynamic weight mechanism for knowledge graph nodes, enabling the natural decay and reinforcement of user preferences. By introducing memory strength S and last access time, EchoKG dynamically manages the lifecycle of memories. Experimental results on the fully open-source dataset EchoCar-Public demonstrate that compared to MemoryBank, static knowledge graphs, and GPT-4o Memory, EchoKG reduces the average context length by 32%, increases the F1 score for intent recognition by 5.1%, and improves the personalized consistency score by 0.68 points, while maintaining a response latency within 800ms.