Abstract
Online rating systems are widely accepted as means for quality assessment on the web and users increasingly rely on these systems when deciding to purchase an item online. This makes such rating systems frequent targets of attempted manipulation by posting unfair rating scores. Therefore, providing useful, realistic rating scores as well as detecting unfair behavior are both of very high importance. Existing solutions are mostly majority based, also employing temporal analysis and clustering techniques. However, they are still vulnerable to unfair ratings. They also ignore distances between options, the provenance of information, and different dimensions of cast rating scores while computing aggregate rating scores and trustworthiness of users. In this article, we propose a robust iterative algorithm which leverages information in the profile of users and provenance of information, and which takes into account the distance between options to provide both more robust and informative rating scores for items and trustworthiness of users. We also prove convergence of iterative ranking algorithms under very general assumptions, which are satisfied by the algorithm proposed in this article. We have implemented and tested our rating method using both simulated data as well as four real-world datasets from various applications of reputation systems. The experimental results demonstrate that our model provides realistic rating scores even in the presence of a massive amount of unfair ratings and outperforms the well-known ranking algorithms.
- Mohammad Allahbakhsh and Aleksandar Ignjatovic. 2015. An iterative method for calculating robust rating scores. IEEE Transactions on Parallel and Distributed Systems 26, 2 (Feb. 2015), 340--350.Google Scholar
Cross Ref
- Mohammad Allahbakhsh, Aleksandar Ignjatovic, Hamid Reza Motahari-Nezhad, and Boualem Benatallah. 2015. Robust evaluation of products and reviewers in social rating systems. WWW 18, 1 (2015), 73--109. Google Scholar
Digital Library
- Erman Ayday, Hanseung Lee, and Faramarz Fekri. 2009. An iterative algorithm for trust and reputation management. In Proceedings of the 2009 IEEE International Conference on Symposium on Information Theory - Volume 3 (ISIT’09). 2051--2055. Google Scholar
Digital Library
- Iván Cantador, Peter Brusilovsky, and Tsvi Kuflik. 2011. In Proceedings of the 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) (RecSys’2011). ACM. Google Scholar
Digital Library
- Gianluca Ciccarelli and Renato Lo Cigno. 2011. Collusion in peer-to-peer systems. Computer Networks 55, 15 (2011), 3517--3532. Google Scholar
Digital Library
- Cristobald de Kerchove and Paul Van Dooren. 2010. Iterative filtering in reputation systems. SIAM Journal on Matrix Analysis and Applications 31, 4 (2010), 1812--1834. Google Scholar
Digital Library
- Shehroze Farooqi, Guillaume Jourjon, Muhammad Ikram, Mohamed Ali Kaafar, Emiliano De Cristofaro, Zubair Shafiq, Arik Friedman, and Fareed Zaffar. 2017. Characterizing key stakeholders in an online black-hat marketplace. In Proceedings of the 2017 APWG Symposium on Electronic Crime Research (eCrime). 17--27.Google Scholar
Cross Ref
- Benjamin Fauth, Jasmin Decristan, Svenja Rieser, Eckhard Klieme, and Gerhard Büttner. 2014. Student ratings of teaching quality in primary school: Dimensions and prediction of student outcomes. Learning and Instruction 29, 0 (2014), 1--9.Google Scholar
Cross Ref
- Amir Fayazi, Kyumin Lee, James Caverlee, and Anna Squicciarini. 2015. Uncovering crowdsourced manipulation of online reviews. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’15). 233--242. Google Scholar
Digital Library
- Ardelio Galletti, Giulio Giunta, and G. Schmid. 2012. A mathematical model of collaborative reputation systems. Int. Journal of Computational Mathematics 89, 17 (Nov. 2012), 2315--2332. Google Scholar
Digital Library
- Maria Glenski and Tim Weninger. 2017. Rating effects on social news posts and comments. ACM Transactions on Intelligent Systems Technology 8, 6 (July 2017), 78:1--78:19. Google Scholar
Digital Library
- Kevin Hoffman, David Zage, and Cristina Nita-Rotaru. 2009. A survey of attack and defense techniques for reputation systems. ACM Computing Surveys 42, 1, Article 1 (Dec. 2009), 31 pages. Google Scholar
Digital Library
- Zan Huang, Daniel Zeng, and Hsinchun Chen. 2007. A comparison of collaborative-filtering recommendation algorithms for e-commerce. IEEE Intelligent Systems 22, 5 (Sept. 2007), 68--78. Google Scholar
Digital Library
- Sepandar D. Kamvar, Mario T. Schlosser, and Hector Garcia-Molina. 2003. The eigentrust algorithm for reputation management in P2P networks. In Proceedings of the 12th International Conference on World Wide Web (WWW’03). 640--651. Google Scholar
Digital Library
- Srijan Kumar, Bryan Hooi, Disha Makhija, Mohit Kumar, Christos Faloutsos, and V. S. Subrahmanian. 2017. FairJudge: Trustworthy user prediction in rating platforms. CoRR abs/1703.10545 (2017).Google Scholar
- Amy N. Langville and Carl D. Meyer. 2012. Google’s PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press. Google Scholar
Digital Library
- Paolo Laureti, L. Moret, Yi-Cheng Zhang, and Yi-Kuo Yu. 2006. Information filtering via iterative refinement. EPL (Europhysics Letters) 75 (Sept. 2006), 1006--1012. arXiv:arXiv:physics/0608166Google Scholar
- Lingfang Ivy Li, Steven Tadelis, and Xiaolan Zhou. 2016. Buying Reputation as a Signal of Quality: Evidence from an Online Marketplace. Technical Report. National Bureau of Economic Research.Google Scholar
- Rong-Hua Li, Jeffrey Xu Yu, Xin Huang, and Hong Cheng. 2012. Robust reputation-based ranking on bipartite rating networks. In Proceedings of the 2012 SIAM International Conference on Data Mining (SDM’12). 612--623.Google Scholar
Cross Ref
- Qiao Lian, Zheng Zhang, Mao Yang, Ben Y Zhao, Yafei Dai, and Xiaoming Li. 2007. An empirical study of collusion behavior in the Maze P2P file-sharing system. In Proceedings of the 27th IEEE International Conference on Distributed Computing Systems (ICDCS’07). 56--56. Google Scholar
Digital Library
- Ee-Peng Lim, Viet-An Nguyen, Nitin Jindal, Bing Liu, and Hady Wirawan Lauw. 2010. Detecting product review spammers using rating behaviors. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management. ACM, 939--948. Google Scholar
Digital Library
- Yuhong Liu, Yafei Yang, and Yan Lindsay Sun. 2008. Detection of collusion behaviors in online reputation systems. In Proceedings of the 2008 42nd Asilomar Conference on Signals, Systems and Computers. IEEE, 1368--1372.Google Scholar
Cross Ref
- Matus Medo and Joseph R. Wakeling. 2010. The effect of discrete vs. continuous-valued ratings on reputation and ranking systems. CoRR abs/1001.3745 (2010).Google Scholar
- Arjun Mukherjee, Bing Liu, and Natalie Glance. 2012. Spotting fake reviewer groups in consumer reviews. In Proceedings of the 21st International Conference on World Wide Web (WWW’12). 191--200. Google Scholar
Digital Library
- Mohsen Rezvani, Aleksandar Ignjatovic, Elisa Bertino, and Sanjay Jha. 2013. A robust iterative filtering technique for wireless sensor networks in the presence of malicious attacks. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (SenSys’13). Article 30, 2 pages. Google Scholar
Digital Library
- Mohsen Rezvani, Aleksandar Ignjatovic, Elisa Bertino, and Sanjay Jha. 2015. Secure data aggregation technique for wireless sensor networks in the presence of collusion attacks. IEEE Transactions on Dependable and Secure Computing 12, 1 (Jan. 2015), 98--110.Google Scholar
Digital Library
- Mohsen Rezvani, Aleksandar Ignjattovic, and Elisa Bertino. 2017. A Provenance-Aware Multi-Dimensional Reputation System For Online Rating Systems. Technical Report CERIAS TR 2017-2. Purdue University, West Lafayette, IN.Google Scholar
- Yan Sun and Yuhong Liu. 2012. Security of online reputation systems: The evolution of attacks and defenses. IEEE Signal Processing Magazine 29, 2 (March 2012), 87 --97.Google Scholar
- Yan Lindsay Sun and Yuhong Liu. 2012. Security of online reputation systems: The evolution of attacks and defenses. IEEE Signal Processing Magazine 29, 2 (2012), 87--97.Google Scholar
Cross Ref
- Steven Tadelis. 2016. The economics of reputation and feedback systems in e-commerce marketplaces. IEEE Internet Computing 20, 1 (Jan. 2016), 12--19. Google Scholar
Digital Library
- Jiliang Tang, Huiji Gao, and Huan Liu. 2012. mTrust: Discerning multi-faceted trust in a connected world. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining (WSDM’12). 93--102. Google Scholar
Digital Library
- Gang Wang, Christo Wilson, Xiaohan Zhao, Yibo Zhu, Manish Mohanlal, Haitao Zheng, and Ben Y. Zhao. 2012. Serf and turf: Crowdturfing for fun and profit. In Proceedings of the 21st International Conference on World Wide Web (WWW’12). 679--688. Google Scholar
Digital Library
- Xinlei Oscar Wang, Wei Cheng, Prasant Mohapatra, and Tarek F. Abdelzaher. 2013. ARTSense: Anonymous reputation and trust in participatory sensing. In Proceedings of the 2013 IEEE INFOCOM. 2517--2525.Google Scholar
- Haitao Xu, Daiping Liu, Haining Wang, and Angelos Stavrou. 2017. An empirical investigation of ecommerce-reputation-escalation-as-a-service. ACM Trans. Web 11, 2 (May 2017), 13:1--13:35. Google Scholar
Digital Library
- Yafei Yang, Qinyuan Feng, Yan Lindsay Sun, and Yafei Dai. 2008. RepTrap: A novel attack on feedback-based reputation systems. In Proceedings of the 4th ACM International Conference on Security and Privacy in Communication Networks (SecureComm’08). Article 8, 11 pages. Google Scholar
Digital Library
- Ya-Fei Yang, Qin-Yuan Feng, Yan Sun, and Ya-Fei Dai. 2009. Dishonest behaviors in online rating systems: Cyber competition, attack models, and attack generator. J. Comput. Sci. Technol. 24, 5 (2009), 855--867. Google Scholar
Digital Library
- Yan-Bo Zhou, Ting Lei, and Tao Zhou. 2011. A robust ranking algorithm to spamming. EPL (Europhysics Letters) 94, 4 (2011), 48002--48007.Google Scholar
Cross Ref
Index Terms
A Provenance-Aware Multi-dimensional Reputation System for Online Rating Systems
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