Name
Title | ||
|---|---|---|
Generating Behavior Features for Cold-Start Spam Review Detection with Adversarial Learning |
Abstract | ||
|---|---|---|
Due to the wide applications, spam detection has long been a hot research topic in both academia and industry. Existing studies show that behavior features are effective in distinguishing the spam and legitimate reviews. However, it usually takes a long time to collect such features and thus is hard to apply them to cold-start spam review detection tasks. Recent advances leveraged the neural network to encode the various types of textual, behavior, and attribute information for this task. However, the inherent problem, i.e., lack of effective behavior features for new users who post just one review, is still unsolved. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.ins.2020.03.063 | Information Sciences |
Keywords | DocType | Volume |
Spam review detection,Cold-start problem,Generative adversarial network | Journal | 526 |
ISSN | Citations | PageRank |
0020-0255 | 1 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoya Tang | 1 | 22 | 2.25 |
| Tieyun Qian | 2 | 177 | 28.81 |
| Zhenni You | 3 | 2 | 2.05 |