Toward Trustworthy Social Network Services: A Robust Design of Recommender Systems |
Noh, Giseop
(Dept. of Computer Science and Engineering, Seoul National University)
Oh, Hayoung (School of Electronic and Engineering, Soongsil University) Lee, Kyu-haeng (Dept. of Computer Science and Engineering, Seoul National University) Kim, Chong-kwon (Dept. of Computer Science and Engineering, Seoul National University) |
1 | H. F. Yu et al., "SybilLimit: A near-optimal social network defense against Sybil attacks," IEEE-ACM Trans. Netw., vol. 18, pp. 885-898, June 2010. DOI |
2 | N. Tran et al., "Sybil-resilient online content voting," in Proc. 6th USENIX Symp. on Netw. Syst. Design and Implementation, 2009, pp. 15-28. |
3 | N. Tran et al., "Optimal sybil-resilient node admission control," in Proc. IEEE INFOCOM, 2011, pp. 3218-3226. |
4 | W. Wei et al., "Sybildefender: Defend against sybil attacks in large social networks," in Proc. IEEE INFOCOM, 2012, pp. 1951-1959. |
5 | Z. Gyongyi, H. Garcia-Molina, and J. Pedersen, "Combating web spam with trustrank," in Proc. 30th Int. Conf. Very Large Data Bases, vol. 30, 2004, pp. 576-587. |
6 | M. Sobek. (2002). PR0 - Google's PageRank 0 Penalty [Online]. Available: http://pr.efactory.de/e-pr0.shtml |
7 | S. Ghosh et al., "Understanding and combating link farming in the twitter social network," in Proc. 21st Int. Conf. World Wide Web, 2012, pp. 61-70. |
8 | N. Z. Gong, M. Frank, and P. Mittal, "SybilBelief: A semi-supervised learning approach for structure-based Sybil detection," IEEE Trans. Inform. Forensics Security, 2014. |
9 | Egele et al., "COMPA: Detecting ompromised accounts on social networks," NDSS. 2013. |
10 | J. Meng et al., "Inferring Strange Behavior from Connectivity Pattern in Social Networks," Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2014. pp. 126-138. |
11 | H. Yu et al., "Dsybil: Optimal sybil-resistance for recommendation systems," in Proc. 30th IEEE Symp. Security and Privacy, 2009, pp. 283-298. |
12 | G. Noh et al., "Robust Sybil attack defense with information level in online recommender systems," Expert Systems with Applications. 2014;41(4, Part 2):1781-1791. DOI |
13 | B. Mobasher, R. Burke, and J. J. Sandvig, "Model-based collaborative filtering as a defense against profile injection attacks," in Proc. Nat. Conf. Artificial Intell., 2006, p. 1388. |
14 | M. Jamali and M. Ester, "A matrix factorization technique with trust propagation for recommendation in social networks," in Proc. 4th ACM Conf. Recommender Syst., 2010, pp. 135-142. |
15 | V. R. Kagita, A. K. Pujari, and V. Padmanabhan, "Virtual user approach for group recommender systems using precedence relations," Information Sciences 294 (2015): 15-30. DOI |
16 | Mislove et al., "Measurement and analysis of online social networks," in Proc. 7th ACM SIGCOMM Conf. Internet Measurement, 2007. |
17 | Clauset et al., "Power-law distributions in empirical data," SIAM review 51.4 (2009): 661-703. DOI |
18 | G. Noh et al., "PSD: Practical Sybil Detection Schemes Using Stickiness and Persistence in Online Recommender Systems," Information Sciences, 2014. |
19 | N. Lathia, S. Hailes, and L. Capra, "Temporal defenses for robust recommendations," in Proc. PSDML, Barcelona, Spain, 2011. |
20 | J. Douceur, "The Sybil Attack" in Peer-to-peer Systems, Germany, Heidelberg: Springer, 2002, pp. 251-260. |
21 | B. Mehta and W. Nejdl, "Attack resistant collaborative filtering," in Proc. 31st Annu. Int. ACM SIGIR Conf. Research and Development in Inform. Retrieval, 2008, pp. 75-82. |
22 | I. T. Jolliffe. Principal Component Analysis, 2nd Ed. Springer, 2002. |
23 | Y. Koren, "Factorization meets the neighborhood: A multifaceted collaborative filtering model," in Proc. 14th ACM SIGKDD Int. Conf. Knowl. Discovery and Data Mining, Aug. 2008, pp. 426-434. |
24 | P. Melville and V. Sindhwani, "Recommender systems," Encyclopedia of machine learning, pp. 829-838, 2010. |
25 | Z. Cheng and N. Hurley, "Robust collaborative recommendation by least trimmed squares matrix factorization," in Proc. 22nd IEEE Int. Conf. ICTAI, 2010, pp. 105-112. |
26 | J. M. Kleinberg, "Authoritative sources in a hyperlinked environment," J. ACM, vol. 46, pp. 604-632, 1999. DOI |
27 | B.Mobasher et al., "Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness," ACM Trans. Internet Technol., vol. 7, p. 23, 2007. DOI |
28 | P. Resnick et al., "GroupLens: An open architecture for collaborative filtering of netnews," in Proc. ACM conf. Comput. Supported Cooperative Work, 1994, pp. 175-186. |
29 | G. Linden, B. Smith, and J. York, "Amazon.com recommendations: Itemto-item collaborative filtering," IEEE Internet Comput., vol. 7, pp. 76-80, 2003. DOI |
30 | Y. Koren, R. Bell, and C. Volinsky, "Matrix factorization techniques for recommender systems," Computer, vol. 42, pp. 30-37, 2009. |
31 | P. Melville and V. Sindhwani, "Recommender systems," Encyclopedia of machine learning, pp. 829-838, 2010. |
32 | K. Lang, "NewsWeeder: Learning to filter netnews," in Proc. 12th Int. Conf. Mach. Learn., San Mateo, CA, USA, 1995, pp. 331-339. |
33 | P. Melville, R. J. Mooney, and R. Nagarajan, "Content-boosted collaborative filtering for improved recommendations," in Proc. Nat. Conf. Artificial Intell., 2002, pp. 187-192. |
34 | H. F. Yu et al., "SybilGuard: Defending against sybil attacks via social networks," IEEE-ACM Trans. Netw., vol. 16, no. 3, pp. 576-589, June 2008. DOI |
35 | C. Yang et al., "Analyzing spammers' social networks for fun and profit: A case study of cyber criminal ecosystem on twitter," in Proc. 21st Int. Conf. World Wide Web, 2012, pp. 71-80. |
36 | B. Mehta, T. Hofmann, and P. Fankhauser, "Lies and propaganda: Detecting spam users in collaborative filtering," in Proc. 12th Int. Conf. IUI, 2007, pp. 14-21. |
37 | Pujahari, Abinash, and Vineet Padmanabhan, "A New Grouping Method Based on Social Choice Strategies for Group Recommender System," Computational Intelligence in Data Mining-Volume 1. Springer India, 2015. pp. 325-332. |