Attention-driven Graph-Sequence Fusion Network for Next Point-of-Interest Recommendation

Runzhe Tao; Weibin Guo; Jie Mei; Xin Liu

doi:10.1109/icicml63543.2024.10957767

ScienceGate Book Chapters

JOURNAL ARTICLE

Attention-driven Graph-Sequence Fusion Network for Next Point-of-Interest Recommendation

Runzhe Tao Weibin Guo Jie Mei Xin Liu

Year: 2024 Pages: 1958-1964

DOI: 10.1109/icicml63543.2024.10957767

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Sequence (biology) Graph Fusion Point (geometry) Point of interest Graph theory Theoretical computer science Artificial intelligence Mathematics Combinatorics Chemistry

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.29

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Advanced Graph Neural Networks

Physical Sciences → Computer Science → Artificial Intelligence

Recommender Systems and Techniques

Physical Sciences → Computer Science → Information Systems

Brain Tumor Detection and Classification

Life Sciences → Neuroscience → Neurology

Attention-driven Graph-Sequence Fusion Network for Next Point-of-Interest Recommendation

Metrics

Topics

Related Documents

Memory-Enhanced Gated Graph Attention Network for Next Point-of-Interest Recommendation

Memory Augmented Hierarchical Attention Network for Next Point-of-Interest Recommendation

Global spatio-temporal aware graph neural network for next point-of-interest recommendation

A Geographical-Temporal Awareness Hierarchical Attention Network for Next Point-of-Interest Recommendation

Improving the spatial–temporal aware attention network with dynamic trajectory graph learning for next Point-Of-Interest recommendation