Cold-Start Visualization Recommendation Driven by Large Language Models for Ocean Data Analysis

Xin LiJixiu LiaoWen Liu^*Yu MiaoLeyu WangShuqing Sun

• China University of Petroleum(East China), Qingdao, Shandong Province, China

Marine data is typically large-scale and complex, requiring effective visualization recommendation systems for data filtering and value extraction. The primary challenge in visualization automatic recommendation lies in the conflict between the inherent ambiguity of user intentions and the limitations of precise interaction methods. In the initial phase, users often lack well-defined analytical goals for the dataset, necessitating a cold-start and iterative interaction to clarify their goals. Moreover, although existing interaction methods are diverse, their precise control fails to effectively convey users' ambiguous intentions. To address these issues, we introduce a novel cold-start visualization recommendation system that integrates a Large Language Model (LLM) and a Grammar Variational Autoencoder (GVAE). The LLM generates initial exploratory goals and visualization recommendations based on data descriptions, while the GVAE produces visual summary projections to verify the extent of user intent fulfillment.Additionally, users can roll back to previous record point to establish new analytical paths. This forms a comprehensive analysis framework for observation, reasoning, and backtracking. Users can adjust their exploration goals and refine their intent expressions based on projections through the LLM, iterating until the analysis is complete. The GVAE analyzes chart correlations and latent patterns, while the LLM converts ambiguous intentions into precise representations, with both working together to address the cold-start problem. The effectiveness of this method in cold-start visualization recommendations and semantic-driven interactions has been validated through case studies and evaluations.