POI recommendation — 3 Technical Articles

Nov 4, 2025 · Artificial Intelligence

Spacetime‑GR: AI‑Powered Spatiotemporal Model Transforming POI Recommendations

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.

AmapGenerative AIPOI recommendation

0 likes · 14 min read

Spacetime‑GR: AI‑Powered Spatiotemporal Model Transforming POI Recommendations

DataFunSummit

Nov 23, 2021 · Artificial Intelligence

Travel Intention‑Aware Out‑of‑Town POI Recommendation (TRAINOR) Framework

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.

POI recommendationcold startgraph neural network

0 likes · 16 min read

Travel Intention‑Aware Out‑of‑Town POI Recommendation (TRAINOR) Framework

DataFunTalk

Oct 29, 2021 · Artificial Intelligence

Travel Intention‑Aware Out‑of‑Town POI Recommendation (TRAINOR) Framework

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.

POI recommendationcold startgraph neural network

0 likes · 15 min read