该仓库旨在整理 Next-Step Location Prediction 任务的相关文献，供给自己科研学习使用
This repo aims to organize related works for the topic "Next-Step Location Prediction" for scientist research.
The repo will divide these works into three parts:
- classic assumptions/ theory/ findings
- topics (related data)
- models (mainly based on neural networks)
| Paper | Journal & Year | Findings | Main Method |
|---|---|---|---|
| Limits of Predictability in Human Mobility | Science, 2010 | The predictability of the general public is around 93%. | Entorpy / Π |
| Paper | Journal & Year | Topic | Findings | Main Method |
|---|---|---|---|---|
| Trajectory Data Mining: An overview | ACM Transactions on Intelligent Systems and Technology, 2015 | Trajectory Data | Overview on trajectory data | Introduction and Main methods on different trajectory-related tasks |
| Understanding the movement predictability of international travelers using a nationwide mobile phone dataset collected in South Korea | CEUS, 2022 | Travelers' predictability | Despite travelling, tourist movement is still predictable | 1-order Markov Chain / LSTM |
| Paper | Journal & Year | Findings | Innovation | Main Method |
|---|---|---|---|---|
| Context-aware multi-head self-attentional neural network model for next location prediction | TRC, 2023 | - | 1. Adding user ID | - |
| DeepApp: Predicting Personalized smartphone app usage via context-aware multi-task Learning | ACM Transactions on Intelligent Systems and Technology, 2020 | SOTA | Multi-task Learning, others as fake task | Multi-task |