6 papers · 1 filter
SPAR: Enhancing Industrial-Scale Generative POI Recommendation via Real-World Spatial Perception
Fangye Wang, Yunjin Gu, Haowen Lin +4
Generative Point-of-Interest (POI) recommendation, autoregressively generating a target POI's semantic ID (SID), holds great promise for Location-Based Services, where a recommenda…
HF-SID: High-Fidelity Semantic IDs for Generative Retrieval in Location-Based Services
Haowen Lin, Jing Li, Zhibin Hao +5
Generative retrieval has attracted increasing attention in Location-Based Services (LBS), where each Point-of-Interest (POI) is represented as a Semantic ID (SID). As the SID is th…
GR2 Technical Report
Yufei Li, Zaiwei Zhang, Mingfu Liang +67
Industrial recommendation systems serve billions of users through a multi-stage funnel -- retrieval, early-stage ranking, and re-ranking -- where the final re-ranking step dispropo…
GeoGR: Enabling Spatio-Temporal Aware Industrial-scale Generative POI Recommendations
Fangye Wang, Haowen Lin, Yifang Yuan +4
Next Point-of-Interest (POI) prediction is a fundamental task in location-based services (LBS), especially critical for large-scale navigation platforms such as AMAP that serve bil…
HeMix: Scaling Industrial Ranking Models with Heterogeneous Token Mixing
Fangye Wang, Guowei Yang, Xiaojiang Zhou +2
Scaling up ranking models for industrial recommender systems faces two critical challenges: (C1) existing sequence tokenization fails to jointly capture context-aware and context-i…
GR2: Generative Reasoning Re-ranker
Mingfu Liang, Yufei Li, Jay Xu +20
Recent studies increasingly explore Large Language Models (LLMs) as a new paradigm for recommendation systems due to their scalability and world knowledge. However, existing work h…