文献类型：期刊文献

英文题名：Attention-driven Graph-Sequence Fusion Network for Next Point-of-Interest Recommendation

作者：Tao, Runzhe[1]; Guo, Weibin[1]; Jie, Mei[2]; Liu, Xin[3]

机构：[1] East China University of Science and Technology, School of Information Science and Engineering, Shanghai, China; [2] Zhengzhou University, School of Computer and Artificial Intelligence, Zhengzhou, China; [3] Central China Normal University, Faculty of Artificial Intelligence in Education, Wuhan, China

年份：2024

起止页码：1958

外文期刊名：2024 International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 2024

收录：EI(收录号：20251918395262)

语种：英文

外文关键词：Bayesian networks - Directed graphs - Graph algorithms - Network theory (graphs)

摘要：Next Point-of-Interest (POI) recommendation has been widely applied on social network-based platforms, aiming to recommend a user's potential next location based on their historical check-in data. Current research faces limitations in both graph neural networks and sequential learning models, with challenges in fully extracting user check-in features and effectively integrating global and personal information. To address these issues, we propose a model that fuses features obtained from a graph neural network and a sequential encoder through an attention fusion mechanism, followed by learning with a hierarchical Transformer encoder to enhance feature representation. Our model has been extensively tested on three datasets, achieving promising results. ? 2024 IEEE.