AttnMove: History Enhanced Trajectory Recovery via Attentional Network
Authors: Tong Xia, Yunhan Qi, Jie Feng, Fengli Xu, Funing Sun, Diansheng Guo, Yong Li4494-4502
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our model on two real-world datasets, and extensive results demonstrate the performance gain compared with the state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Tong Xia1, Yunhan Qi1, Jie Feng1, Fengli Xu1, Funing Sun2, Diansheng Guo2, Yong Li1 1Beijing National Research Center for Information Science and Technology, Tsinghua University. 2Tencent Corporation. |
| Pseudocode | Yes | Algorithm 1: Training Algorithm for Attn Move |
| Open Source Code | Yes | Codes available in https://github.com/XTxiatong/Attn Move |
| Open Datasets | Yes | Geolife3: This open data is collected from Microsoft Research Asia Geolife project by 182 users from April 2007 to August 2012 over all the world. Each trajectory is represented by a sequence of time-stamped points, containing longitude and altitude (Zheng et al. 2010). https://www.microsoft.com/en-us/research/project/geolife-building-social-networks-using-human-location-history/ |
| Dataset Splits | Yes | We sort each user s trajectories by time, and take the first 70% as the training set from the fourth day (to guarantee that each trajectory has at least three days as history), the following 10% as the validation set and the remaining 20% as the test set. |
| Hardware Specification | Yes | We train our model on a linux server with a TITAN Xp GPU (12 G Memory) and a Intel(R) Xeon(R) CPU @ 2.20GHz. |
| Software Dependencies | No | The paper mentions "implemented by Python and Tensorflow (Abadi et al. 2016)", but does not provide specific version numbers for Python or TensorFlow. |
| Experiment Setup | Yes | The regularization factor λ is set as 0.01. ... we set time interval as 30 minutes for both two datasets. |