Generative Spatiotemporal Intent Sequence Recommendation via Implicit Reasoning in Amap
Real-world user behavior rarely consists of isolated actions; instead, it often forms intent flows governed by spatiotemporal dependencies. To provide integrated service recommendations, we focus on the task of Generative Spatiotemporal Intent Sequence Recommendation (GSISR), which aims to generate intent sequences that are logically coherent and physically executable within complex spatiotemporal contexts. While LLMs offer strong reasoning potential for GSISR, direct industrial deployment is limited by high inference latency and context-mismatched or physically infeasible plans. To address these challenges, we propose a generative framework, GPlan, that internalizes LLM reasoning into li...
| Authors | Sicong Wang, Ruiting Dong, Yue Liu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 27 May 2026 |
| Last updated at source | 8 October 2026 |
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