Name
Title | ||
|---|---|---|
Predicting Mouse Click Position Using Long Short-Term Memory Model Trained by Joint Loss Function |
Abstract | ||
|---|---|---|
BSTRACT Knowing where users might click in advance can potentially improve the efficiency of user interaction in desktop user interfaces. In this paper, we propose a machine learning approach to predict mouse click location. Our model, which is LSTM (long short-term memory)-based and trained by joint supervision, can predict the rectangular region of mouse click with feeding mouse trajectories on the fly. Experiment results show that our model can achieve a result of a predicted rectangle area of 58 × 79 pixels with 92% accuracy, and reduce prediction error when compared with other state-of-the-art prediction methods using a multi-user dataset. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3411763.3451651 | Conference on Human Factors in Computing Systems |
Keywords | DocType | Citations |
User Intention, Mouse Interaction, Mouse Prediction, Machine Learning | Conference | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Datong Wei | 1 | 5 | 1.05 |
| Chaofan Yang | 2 | 19 | 3.76 |
| Xiaolong (Luke) Zhang | 3 | 1 | 0.35 |
| Xiaoru Yuan | 4 | 1157 | 70.28 |