Amap's Spacetime-GR introduces a spatiotemporal-aware generative framework for industrial-scale POI recommendation, using hierarchical POI encoding, explicit spatiotemporal tokenization, and a three-stage training pipeline to achieve significant CTR and CVR gains in online deployment at ICDM 2026.
This article introduces Spacetime‑GR, a large‑scale generative recommendation model that integrates hierarchical geographic POI indexing and spatiotemporal token encoding to enhance POI prediction for Amap, detailing its pre‑training pipeline, data cleaning, curriculum learning strategy, experimental results, scaling law observations, and the resulting improvements in hit rate and discovery rate.
This article presents TRAINOR, a travel‑intention‑aware out‑of‑town POI recommendation framework that tackles cold‑start, interest‑drift, and geographical gaps by jointly modeling hometown preferences with graph neural networks, neural topic models for travel intention, and matrix‑factorization‑based out‑of‑town preference transfer, and validates its superiority through extensive cross‑city experiments.
This article proposes TRAINOR, a travel‑intention‑aware out‑of‑town POI recommendation framework that tackles cold‑start and interest‑drift challenges by integrating graph neural networks for hometown preference, neural topic models for generic travel intentions, personalized intention inference, geographic modeling, and a preference‑transfer MLP, validated on real cross‑city check‑in data with superior recall performance.
