Where shall we meet? Group meeting location recommendation via preference-mobility reasoning with MEET-LLM AITranslate
Abstract AITranslate
Large Language Models (LLMs) are propelling Intelligent Transportation Systems (ITS) toward an agentic paradigm, enabling intent understanding, constraint-aware reasoning, and decision-making. This paper focuses on the Group Meeting Location Recommendation (GMLR) problem, which aims to jointly optimize group preferences and mobility costs under real-world transportation network constraints. However, existing methods often overlook real-world geographic context, fail to jointly coordinate group preferences and spatial feasibility, and rely heavily on static, structured data to model group preferences, limiting their applicability to complex and dynamic group scenarios. We propose MEET-LLM, an agentic, LLM-driven, closed-loop framework for GMLR. MEET-LLM comprises three modules: (1) a Preference Profiling Module that constructs structured semantic representations of users and merchants by using LLMs to extract multilevel preferences from natural language reviews, overcoming the limitations of static aggregation; (2) a Spatial Optimization Module that introduces a mobility cost (MC) metric to evaluate group-level travel convenience and account for user-specific travel constraints; and (3) a Chain-of-Thought (CoT) Reasoning Module that coordinates group preferences and mobility costs through multi-turn LLM reasoning to generate interpretable, executable recommendations with map-based navigation guidance. MEET-LLM jointly coordinates group preferences and spatial feasibility, bridging natural language reasoning with real-world mobility execution. It enables dynamic, structured, and action-ready recommendations in real urban environments, highlighting the potential of LLMs as the cognitive core for agentic mobility coordination in ITS.
KeyWords AITranslate
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Basic Information:
DOI：10.23919/CHAIN.2026.000007
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Citation Information:
Large Language Models (LLMs) are propelling Intelligent Transportation Systems (ITS) toward an agentic paradigm, enabling intent understanding, constraint-aware reasoning, and decision-making. This paper focuses on the Group Meeting Location Recommendation (GMLR) problem, which aims to jointly optimize group preferences and mobility costs under real-world transportation network constraints. However, existing methods often overlook real-world geographic context, fail to jointly coordinate group preferences and spatial feasibility, and rely heavily on static, structured data to model group preferences, limiting their applicability to complex and dynamic group scenarios. We propose MEET-LLM, an agentic, LLM-driven, closed-loop framework for GMLR. MEET-LLM comprises three modules: (1) a Preference Profiling Module that constructs structured semantic representations of users and merchants by using LLMs to extract multilevel preferences from natural language reviews, overcoming the limitations of static aggregation; (2) a Spatial Optimization Module that introduces a mobility cost (MC) metric to evaluate group-level travel convenience and account for user-specific travel constraints; and (3) a Chain-of-Thought (CoT) Reasoning Module that coordinates group preferences and mobility costs through multi-turn LLM reasoning to generate interpretable, executable recommendations with map-based navigation guidance. MEET-LLM jointly coordinates group preferences and spatial feasibility, bridging natural language reasoning with real-world mobility execution. It enables dynamic, structured, and action-ready recommendations in real urban environments, highlighting the potential of LLMs as the cognitive core for agentic mobility coordination in ITS.
quote
| GB/T 7714-2015 | [1] Shuang Yang, Lening Wang, Zhiyong Cui, et al. Where shall we meet? Group meeting location recommendation via preference-mobility reasoning with MEET-LLM[J]. Chain, 2026, 3(2): 187-203. DOI:10.23919/CHAIN.2026.000007. |
| MLA | [1] Shuang Yang, et al., "Where shall we meet? Group meeting location recommendation via preference-mobility reasoning with MEET-LLM." Chain, vol. 3, no. 2, 2026, pp. 187-203, https://doi.org/10.23919/CHAIN.2026.000007. |
| APA | [1] Shuang Yang, Lening Wang, Zhiyong Cui, Yilong Ren, Liang Xu, Mohamed Abouelela, Haiyang Yu, & Aoyong Li. (2026). Where shall we meet? Group meeting location recommendation via preference-mobility reasoning with MEET-LLM. Chain, 3(2), 187-203. https://doi.org/10.23919/CHAIN.2026.000007 |
| IEEE | [1] Shuang Yang, Lening Wang, Zhiyong Cui, Yilong Ren, Liang Xu, Mohamed Abouelela, Haiyang Yu, and Aoyong Li, "Where shall we meet? Group meeting location recommendation via preference-mobility reasoning with MEET-LLM," Chain, vol. 3, no. 2, pp. 187-203, 2026, doi: 10.23919/CHAIN.2026.000007. keywords: {Chain-of-Thought Reasoning;Group Meeting Location Recommendation (GMLR);Intelligent Transportation Systems (ITS);Large Language Models (LLMs)} |