DatasetCollection

collection for the common dataset in my research

Recommender systems

Social Recommendations

Data Set	Basic Meta					User Context
Data Set	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

Spammer detection

Social Network

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.

Shilling Detection

Data Set	Non-spammer	Spammer	Introduction
Amazon [6]	3,118	1,937	N/A

Reference

[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)

[6]. Xu, Chang, et al. "Uncovering collusive spammers in Chinese review websites." ACM International Conference on Conference on Information & Knowledge Management ACM, 2013:979-988.

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