collection for the common dataset in my research
| Data Set | Basic Meta | User Context | ||||||
|---|---|---|---|---|---|---|---|---|
| Users | Items | Ratings (Scale) | Density | Users | Links (Type) | |||
| Ciao [1] | 7,375 | 105,114 | 284,086 | [1, 5] | 0.0365% | 7,375 | 111,781 | Trust |
| Epinions [2] | 40,163 | 139,738 | 664,824 | [1, 5] | 0.0118% | 49,289 | 487,183 | Trust |
| Douban [3] | 2,848 | 39,586 | 894,887 | [1, 5] | 0.794% | 2,848 | 35,770 | Trust |
| Data Set | Non-spammer | Spammer | Introduction |
|---|---|---|---|
| Twitter [4] | 1,295 | 355 | The first column is the user class (i.e., 1 for non-spammers and 2 for spammers) and the subsequent columns numbered from 1 to 62 represent the user characteristics. |
| YouTube [5] | 641 | 31 (promoter) 157(spammer) | The first column is the user class (i.e., 1 for promoters, 2 for spammers, and 3 for legitimates) and the subsequent columns numbered from 1 to 60 represent the user characteristics. |
[1]. Tang, J., Gao, H., Liu, H.: mtrust:discerning multi-faceted trust in a connected world. In: International Conference on Web Search and Web Data Mining, WSDM 2012, Seattle, Wa, Usa, February. pp. 93–102 (2012)
[2]. Massa, P., Avesani, P.: Trust-aware recommender systems. In: Proceedings of the 2007 ACM conference on Recommender systems. pp. 17–24. ACM (2007)
[3]. G. Zhao, X. Qian, and X. Xie, “User-service rating prediction by exploring social users’ rating behaviors,” IEEE Transactions on Multimedia, vol. 18, no. 3, pp. 496–506, 2016.
[4]. Benevenuto, F., Magno, G., Rodrigues, T., & Almeida, V.: Detecting spammers on twitter. In: Collaboration, electronic messaging, anti-abuse and spam conference (CEAS). Vol. 6, No. 2010, p. 12. 2010.
[5]. Benevenuto, F., Rodrigues, T., Almeida, V., Almeida, J., & Gonçalves, M.: Detecting spammers and content promoters in online video social networks. In: Proceedings of the 32nd ACM SIGIR conference on Research and development in information retrieval. pp. 620-627. ACM (2009)