yuemo3353009的博客

[15] Xi, D., Zhuang, F., Liu, Y., Gu, J., Xiong, H., & He, Q. (2019). Modelling of Bi-Directional Spatio-Temporal Dependence and Users’ Dynamic Preferences for Missing POI Check-In Identification. Proceedings of the AAAI Conference on Artificial Intel.

[17] Wang, C., Ma, L., Li, R., Durrani, T. S., & Zhang, H. (2019). Exploring Trajectory Prediction through Machine Learning Methods. IEEE Access, 1–1. doi:10.1109/access.2019.2929430. 文章目录`Abstract``Index terms``1. Introduction``3. 数学背景``4. 基于LSTM的单用.

Lucas May Petry, Camila Leite Da Silva, Andrea Esuli, Chiara Renso & Vania Bogorny (2020): MARC: a robust method for multiple-aspect trajectory classification via space, time, and semantic embeddings, International Journal of Geographical Information .

[1] Mingxiao Li, Feng Lu, Hengcai Zhang & Jie Chen (2018): Predicting future locations of moving objects with deep fuzzy-LSTM networks, Transportmetrica A: Transport Science, DOI: 10.1080/23249935.2018.1552334. To link to this article: https://doi.o.

[1] Zhou F, Yin R, Trajcevski G, et al. Improving human mobility identification with trajectory augmentation[J]. GeoInformatica, 2019: 1-31. GeoInformatica ∈ 计算机科学3区 生词 identification 识别 粗读 文章目录`概述``Abstract``Index words``其他介绍` 概述 ????主要是轨迹分类问题 ????用到了

[1] Liu A, Zhang Y, Zhang X, et al. Representation Learning with Multi-level Attention for Activity Trajectory Similarity Computation[J]. IEEE Transactions on Knowledge and Data Engineering, 2020. 文章目录`Abstract``Index Terms``1 Introduction``2 Preliminari.

[1] Zhang X, Li B, Song C, et al. SASRM: A Semantic and Attention Spatio-temporal Recurrent Model for Next Location Prediction[C]//2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020: 1-8. 文章目录`Abstract``关键词``1 Introduction``2 Rela.

[1] Jin C, Lin Z, Wu M. Augmented Intention Model for Next-Location Prediction from Graphical Trajectory Context[J]. Wireless Communications and Mobile Computing, 2019. 【augment】[ɔːɡˈment] 增加；增大 文章目录`Abstract``1 Introduction``2 预备知识``3 Augment Intent Ne.

[1] Martin H, Bucher D, Suel E, et al. Graph convolutional neural networks for human activity purpose imputation from gps-based trajectory data[J]. 2018. 文章目录`摘要``1 Introduction and Related Work``2 Data and Methods``2.1 Data``2.2 Methods` 摘要 Automatic l.

[1] Yang G, Cai Y, Reddy C K. Spatio-temporal check-in time prediction with recurrent neural network based survival analysis[C]//Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. 2018. 文章目录`摘要``1 Introduction``3.

[1] Bao Y, Huang Z, Li L, et al. A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media[J]. International Journal of Geographical Information Science, 2020: 1-22. 文章目录`摘要``关键词``方法``3.1 模型整体框架``3.2 Clustering using HiSpati.

[1] Liang W, Zhang W. Learning Social Relations and Spatiotemporal Trajectories for Next Check-in Inference[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020. 文章目录`摘要``关键字``II 相关工作``A．Next Check-in Inference``B．Point Process 和 注意力机制``C．.