4 papers
Towards a Densing Law for User Representation Learning at Billion-Scale Capacity
Bin Dou, Junru Zhang, Zhaoyi Yuan +6
User representation learning in real-world industrial scenarios is commonly scaled by increasing user amount, behavioral sequence length and model size. However, existing methods f…
RIPCN: A Road Impedance Principal Component Network for Probabilistic Traffic Flow Forecasting
Haochen Lv, Yan Lin, Shengnan Guo +5
Accurate traffic flow forecasting is crucial for intelligent transportation services such as navigation and ride-hailing. In such applications, uncertainty estimation in forecastin…
Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility Data with Large Language Models
Letian Gong, Yan Lin, Xinyue Zhang +6
Location-based services (LBS) have accumulated extensive human mobility data on diverse behaviors through check-in sequences. These sequences offer valuable insights into users' in…
Spatial-Temporal Cross-View Contrastive Pre-training for Check-in Sequence Representation Learning
Letian Gong, Huaiyu Wan, Shengnan Guo +6
The rapid growth of location-based services (LBS) has yielded massive amounts of data on human mobility. Effectively extracting meaningful representations for user-generated check-…