Name
Abstract | ||
|---|---|---|
Today, app stores offer ranking lists to help users to find quality apps that meet their needs. In order to prevent people from spreading fake reviews which can be used to defame certain apps or manipulate the ranking lists of the app store, we propose a method based on Positive and Unlabeled (PU) learning and behavior density to detect fake reviews. To identify the trusted negative samples, the classifier is trained by the Biased-SVM algorithm. Then, the preliminary screening results of the classifier are combined with user behavior density to identify fake reviews. The traditional fully supervised detection method relies on manually labeled data, the quality of which directly affects the trained classifier. Our proposed method can overcome such a deficiency, and achieve effective learning when there are only a small number of positive samples and a large number of unlabeled samples. Through experiments and case analysis, we demonstrate that our method has high detection accuracy. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/MNET.001.1900542 | IEEE Network |
Keywords | DocType | Volume |
Reliability,Correlation,Supervised learning,Information science,Software engineering,Manuals | Journal | 34 |
Issue | ISSN | Citations |
4 | 0890-8044 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daojing He | 1 | 1013 | 58.40 |
| Menghan Pan | 2 | 0 | 0.34 |
| Kai Hong | 3 | 0 | 0.34 |
| Yao Cheng | 4 | 0 | 1.35 |
| Sammy Chan | 5 | 902 | 66.93 |
| Xiaowen Liu | 6 | 0 | 0.34 |
| Nadra Guizani | 7 | 274 | 32.70 |