Abstract
Rating systems play a vital role in the exponential growth of service-oriented markets. As highly rated online services usually receive substantial revenue in the markets, malicious sellers seek to boost their service evaluation by manipulating the rating system with fake ratings. One effective way to improve the service evaluation is to hire fake rating providers by bribery. The fake ratings given by the bribed buyers influence the evaluation of the service, which further impacts the decision-making of potential buyers. In this paper, we study the bribery of a rating system with multiple sellers and buyers via a game-theoretic perspective. In detail, we examine whether there exists an equilibrium state in the market in which the rating system is expected to be bribery-proof: no bribery strategy yields a strictly positive gain. We first collect real-world data for modeling the bribery problem in rating systems. On top of that, we analyze the problem of bribery in a rating system as a static game. From our analysis, we conclude that at least a Nash equilibrium can be reached in the bribery game of rating systems.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant No. 61906174 and 62172085, in part by the China Postdoctoral Science Foundation under Grant No. 2020M672275, and in part by JSPS KAKENHI Grant Number JP19H04170. We would like to thank Gautham Prakash for his sharing of Google Play apps dataset.
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Zhou, X., Matsubara, S., Liu, Y., Liu, Q. (2022). Bribery in Rating Systems: A Game-Theoretic Perspective. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13282. Springer, Cham. https://doi.org/10.1007/978-3-031-05981-0_6
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