Name
Abstract | ||
|---|---|---|
Automated representation learning is behind many recent success stories in machine learning. It is often used to transfer knowledge learned from a large dataset (e.g., raw text) to tasks for which only a small number of training examples are available. In this paper, we review recent advance in learning to represent social media users in low-dimensional embeddings. The technology is critical for creating high performance social media-based human traits and behavior models since the ground truth for assessing latent human traits and behavior is often expensive to acquire at a large scale. In this survey, we review typical methods for learning a unified user embeddings from heterogeneous user data (e.g., combines social media texts with images to learn a unified user representation). Finally we point out some current issues and future directions. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.24963/ijcai.2019/881 | IJCAI |
Field | DocType | Citations |
Social media,Embedding,Computer science,Artificial intelligence,Multimedia,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shimei Pan | 1 | 684 | 64.41 |
| Tao Ding | 2 | 15 | 8.48 |