Guess Where You Go: Generative Next Point-of-Interest Recommendation in Amap

• URL: http://arxiv.org/abs/2607.26073v1
• Date: Mon, 13 Jul 2026 15:44:06 GMT
• Title: Guess Where You Go: Generative Next Point-of-Interest Recommendation in Amap
• Abstract summary: We propose an end-to-end industrial framework that integrates semantic identifier (SID) generation with generative Point-of-Interest (POI) recommendation.<n>Gwhere is deployed in Amap's homepage service under high-concurrency and low-latency constraints.<n>Long-term online A/B tests show improvements of 5.83% in P-CTR and 6.20% in U-CTR over the production baseline.
• Score: 12.259216278301743
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative retrieval enables recommender systems to retrieve items by generating compact item identifiers, but scaling it to industrial scenarios remains challenging due to redundant or colliding token assignments and insufficient integration of heterogeneous item signals. These challenges are particularly critical for next Point-of-Interest (POI) recommendation, where models must represent structured spatial entities, capture sequential mobility patterns, and produce predictions consistent with real user behavior. We propose Gwhere, an end-to-end industrial framework that integrates semantic identifier (SID) generation with LLM-based generative next POI recommendation. Gwhere first learns discriminative POI SIDs through a contrastive residual-quantization tokenizer that aligns textual, visual, spatial, and collaborative signals. Based on these SIDs, Gwhere adapts LLMs to mobility scenarios via continued pretraining on enriched spatio-temporal corpora, supervised fine-tuning, and Exposure-Aware Kahneman-Tversky Optimization (EAKTO), a reinforcement learning objective for behavioral preference alignment. Experiments on public datasets and Amap's large-scale industrial dataset demonstrate the effectiveness of Gwhere. The system has been deployed in Amap's homepage service under high-concurrency and low-latency constraints. Long-term online A/B tests show improvements of 5.83% in P-CTR and 6.20% in U-CTR over the production baseline. The implementation is publicly available at https://github.com/alibaba/SimCIT.

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