Abstract
Online rating systems are widely accepted as a means for quality assessment on the web, and users increasingly rely on these systems when deciding to purchase an item online. This fact motivates people to manipulate rating systems by posting unfair rating scores for fame or profit. Therefore, both providing useful realistic rating scores as well as detecting unfair behaviours are 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 distance between options, provenance of information and different dimensions of cast rating scores while computing aggregate rating scores and trustworthiness of raters. In this paper, we propose a robust iterative algorithm which leverages the information in the profile of raters, provenance of information and a prorating function for the distance between options to build more robust and informative rating scores for items as well as trustworthiness of raters. We have implemented and tested our rating method using both simulated data as well as three real world datasets. Our tests demonstrate that our model calculates realistic rating scores even in the presence of massive unfair ratings and outperforms well-known ranking algorithms.
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References
Allahbakhsh, M., Ignjatovic, A.: An iterative method for calculating robust rating scores. IEEE Transactions on Parallel and Distributed Systems 26(2), 340–350 (2015)
Hoffman, K., Zage, D., Nita-Rotaru, C.: A survey of attack and defense techniques for reputation systems. ACM Comput. Surv. 42(1), 1:1–1:31 (2009)
Kamvar, S.D., Schlosser, M.T., Garcia-Molina, H.: The eigentrust algorithm for reputation management in P2P networks. In: Proceedings of the 12th International Conference on World Wide Web, pp. 640–651 (2003)
de Kerchove, C., Van Dooren, P.: Iterative filtering in reputation systems. SIAM J. Matrix Anal. Appl. 31(4), 1812–1834 (2010)
Langville, A.N., Meyer, C.D.: Google’s PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press, February 2012
Laureti, P., Moret, L., Zhang, Y.C., Yu, Y.K.: Information filtering via Iterative Refinement. EPL (Europhysics Letters) 75, 1006–1012 (2006)
Lian, Q., Zhang, Z., Yang, M., Zhao, B.Y., Dai, Y., Li, X.: 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 2007, pp. 56–56 (2007)
Lim, E.P., Nguyen, V.A., Jindal, N., Liu, B., Lauw, H.W.: Detecting product review spammers using rating behaviors. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 939–948. ACM (2010)
Liu, Y., Yang, Y., Sun, Y.: Detection of collusion behaviors in online reputation systems. In: 2008 42nd Asilomar Conference on Signals, Systems and Computers, pp. 1368–1372. IEEE (2008)
Morgan, J., Brown, J.: Reputation in online auctions: The market for trust. California Management Review 49(1), 61–81 (2006)
Mukherjee, A., Liu, B., Glance, N.: Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st International Conference on World Wide Web. WWW 2012, pp. 191–200 (2012)
Rezvani, M., Allahbakhsh, M., Ignjatovic, A., Jha, S.: An iterative algorithm for reputation aggregation in multi-dimensional and multinomial rating systems. Tech. Rep. UNSW-CSE-TR-201502, January 2015
Rezvani, M., Ignjatovic, A., Bertino, E., Jha, S.: Secure data aggregation technique for wireless sensor networks in the presence of collusion attacks. IEEE Transactions on Dependable and Secure Computing 12(1), 98–110 (2015)
Sun, Y.L., Liu, Y.: Security of online reputation systems: The evolution of attacks and defenses. IEEE Signal Process. Mag. 29(2), 87–97 (2012)
Tang, J., Gao, H., Liu, H.: mTrust: Discerning multi-faceted trust in a connected world. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining. WSDM 2012, pp. 93–102 (2012)
Wang, G., Wilson, C., Zhao, X., Zhu, Y., Mohanlal, M., Zheng, H., Zhao, B.Y.: Serf and turf: crowdturfing for fun and profit. In: Proceedings of the 21st International Conference on World Wide Web. WWW 2012, pp. 679–688 (2012)
Wang, X.O., Cheng, W., Mohapatra, P., Abdelzaher, T.F.: ARTSense: anonymous reputation and trust in participatory sensing. In: INFOCOM, pp. 2517–2525. IEEE (2013)
Yang, Y.F., Feng, Q.Y., Sun, Y., Dai, Y.F.: Dishonest behaviors in online rating systems: cyber competition, attack models, and attack generator. J. Comput. Sci. Technol. 24(5), 855–867 (2009)
Yang, Y., Feng, Q., Sun, Y.L., Dai, Y.: RepTrap: a novel attack on feedback-based reputation systems. In: Proceedings of the 4th International Conference on Security and Privacy in Communication Netowrks. SecureComm 2008, pp. 8:1–8:11 (2008)
Zhou, Y.B., Lei, T., Zhou, T.: A robust ranking algorithm to spamming. EPL (Europhysics Letters) 94(4), 48002–48007 (2011)
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Rezvani, M., Allahbakhsh, M., Vigentini, L., Ignjatovic, A., Jha, S. (2015). An Iterative Algorithm for Reputation Aggregation in Multi-dimensional and Multinomial Rating Systems. In: Federrath, H., Gollmann, D. (eds) ICT Systems Security and Privacy Protection. SEC 2015. IFIP Advances in Information and Communication Technology, vol 455. Springer, Cham. https://doi.org/10.1007/978-3-319-18467-8_13
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DOI: https://doi.org/10.1007/978-3-319-18467-8_13
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