6 papers
MoE Enhanced Federated Learning for Spatiotemporal Prediction
Zhehao Dai, Xiao Han, Zhaolin Deng +4
Traffic prediction is fundamental to intelligent transportation systems and urban computing, yet many cities continue to suffer from traffic data scarcity due to limited sensor dep…
BlossomRec: Block-level Fused Sparse Attention Mechanism for Sequential Recommendations
Mengyang Ma, Xiaopeng Li, Wanyu Wang +9
Transformer structures have been widely used in sequential recommender systems (SRS). However, as user interaction histories increase, computational time and memory requirements al…
Contextual Attention Modulation: Towards Efficient Multi-Task Adaptation in Large Language Models
Dayan Pan, Zhaoyang Fu, Jingyuan Wang +3
Large Language Models (LLMs) possess remarkable generalization capabilities but struggle with multi-task adaptation, particularly in balancing knowledge retention with task-specifi…
A Dataset for Spatiotemporal-Sensitive POI Question Answering
Xiao Han, Dayan Pan, Xiangyu Zhao +4
Spatiotemporal relationships are critical in data science, as many prediction and reasoning tasks require analysis across both spatial and temporal dimensions--for instance, naviga…
Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework
Xiao Han, Chen Zhu, Xiangyu Zhao +1
Visual geo-localization demands in-depth knowledge and advanced reasoning skills to associate images with precise real-world geographic locations. Existing image database retrieval…
Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning
Xiao Han, Chen Zhu, Xiao Hu +3
Job recommender systems are crucial for aligning job opportunities with job-seekers in online job-seeking. However, users tend to adjust their job preferences to secure employment…