Abstract
Fair evaluation of users is the basic guarantee for the healthy development of the service ecosystem. However, existing methods do not provide an indicator of when can get fair evaluation and how to reduce the proportion of malicious users from the root. This paper proposes a “user-service” double-side evaluation(USDSE) model to solve the problem above. Firstly, we start with getting the reputation of users by using the evaluation of service. Normal and malicious users are distinguished by their reputation. Secondly, we use the minimum number of normal users as the indicator to show when we can get fair evaluation. Finally, the revenue of employing collusive users has been analyzed to reduce the proportion of collusive users indirectly. The simulation experiments show that USDSE effectively improves the accuracy of identifying malicious users and reduces the revenue of employing collusive users.
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References
Akoglu, L., Chandy, R., Faloutsos, C.: Opinion fraud detection in online reviews by network effects. In: ICWSM, vol. 13, pp. 2–11 (2013)
Allahbakhsh, M., Ignjatovic, A.: An iterative method for calculating robust rating scores. IEEE Trans. Parallel Distrib. Syst. 26(2), 340–350 (2015)
Alqwadri, A., Azzeh, M., Almasalha, F.: Appl. Mach. Learn. Online Reput. Syst. 18(3), 11 (2021)
Arora, U., Dutta, H.S., Joshi, B., Chetan, A., Chakraborty, T.: Analyzing and detecting collusive users involved in blackmarket retweeting activities. ACM Trans. Intell. Syst. Technol. 11(3) (2020). https://doi.org/10.1145/3380537https://doi.org/10.1145/3380537
Baek, H., Jang, M., Kim, S.: Who leaves malicious comments on online news? An empirical study in Korea. Journal. Stud. 23(4), 432-447 (2022). https://doi.org/10.1080/1461670X.2022.2031258
Byun, H., Jeong, S., kwon Kim, C.: SC-COM: spotting collusive community in opinion spam detection. Inf. Process. Manag. 58(4), 102593 (2021). https://doi.org/10.1016/j.ipm.2021.102593, https://doi.org/10.1080/1461670X.2022.2031258
Cai, Y., Zhu, D.: Who can we trust: A new approach for fraudulent rater detection in reputation systems. Decis. Sci. 51(1) (2020)
Commerce, B.E., Jøsang, A., Ismail, R.: The beta reputation system. In: In Proceedings of the 15th Bled Electronic Commerce Conference (2002)
Dhawan, S., Gangireddy, S., Kumar, S., Chakraborty, T.: Spotting collective behaviour of online frauds in customer reviews. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-2019 (2019)
Hajek, P., Barushka, A., Munk, M.: Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining. Neural Comput. Appl. 32(1) (2020)
Li, B., Song, R., Liao, L., Liu, C.: A user-oriented trust model for web services. In: 2013 IEEE Seventh International Symposium on Service-Oriented System Engineering, pp. 224–232. IEEE (2013)
Li, J., Ott, M., Cardie, C., Hovy, E.: Towards a general rule for identifying deceptive opinion spam. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 1566–1576 (014). https://doi.org/10.3115/v1/P14-1147
Limam, N., Boutaba, R.: Assessing software service quality and trustworthiness at selection time. IEEE Trans. Softw. Eng. 36(4), 559–574 (2010). https://doi.org/10.1109/TSE.2010.2
Liu, M., Shang, Y., Yue, Q., Zhou, J.: Detecting fake reviews using multidimensional representations with fine-grained aspects plan. IEEE Access 9, 3765–3773 (2021). https://doi.org/10.1109/ACCESS.2020.3047947
Maarouf, I., Baroudi, U., Naseer, A.R.: Efficient monitoring approach for reputation system-based trust-aware routing in wireless sensor networks. IET Commun. 3(5), 846–858 (2009)
Martens, D., Maalej, W.: Towards understanding and detecting fake reviews in app stores. Empir. Softw. Eng. 24(6), 3316–3355 (2019)
Nguyen, H.T., Zhao, W., Yang, J.: A trust and reputation model based on Bayesian network for web services. In: 2010 IEEE International Conference on Web Services, pp. 251–258. IEEE (2010)
Oh, H.K., Jung, J., Park, S., Kim, S.W.: A robust reputation system using online reviews? Comput. Sci. Inf. Syst. 17, 7–7 (2020)
Rezvani, M., Rezvani, M.: A randomized reputation system in the presence of unfair ratings. ACM Trans. Manage. Inf. Syst. 11(1) (2020). https://doi.org/10.1145/3384472, https://doi.org/10.1145/3384472
Sharma, S.S., Dutta, G.: Sentidraw: Using star ratings of reviews to develop domain specific sentiment lexicon for polarity determination. Inf. Process. Manag. 58(1), 102412 (2021)
Tchakounté, F., Pagor, A., Kamgang, J.C., Atemkeng, M.: Ciaa-repdroid: a fine-grained and probabilistic reputation scheme for android apps based on sentiment analysis of reviews. Fut. Internet 12(9), 145 (2020)
Wang, M., Wang, G., Zhang, Y., Li, Z.: A high-reliability multi-faceted reputation evaluation mechanism for online services. IEEE Trans. Serv. Comput. 12, 836–850 (2016)
Wang, N., Yang, J., Kong, X., Gao, Y.: A fake review identification framework considering the suspicion degree of reviews with time burst characteristics. Exp. Syst. Appl. 190, 116207 (2022). https://doi.org/10.1016/j.eswa.2021.116207https://www.sciencedirect.com/science/article/pii/S0957417421015219
Wang, S., Zheng, Z., Wu, Z., Lyu, M.R., Yang, F.: Reputation measurement and malicious feedback rating prevention in web service recommendation systems. IEEE Trans. Serv. Comput. 8(5), 755–767 (2015)
Weng, J., Miao, C., Goh, A.: An entropy-based approach to protecting rating systems from unfair testimonies. IEICE Transactions 89-D, 2502–2511 (09 2006). https://doi.org/10.1093/ietisy/e89-d.9.2502
Wu, X., Jin, L., Xu, Q.: Expertise makes perfect: How the variance of a reviewer’s historical ratings influences the persuasiveness of online reviews. J. Retail. 97(2), 238-250 (2021). https://doi.org/10.1016/j.jretai.2020.05.006, https://www.sciencedirect.com/science/article/pii/S0022435920300270
Yang, B., Liu, Y., Liang, Y., Tang, M.: Exploiting user experience from online customer reviews for product design. Int. J. Inf. Manag. 46, 173-186 (2019). https://doi.org/10.1016/j.ijinfomgt.2018.12.006, https://www.sciencedirect.com/science/article/pii/S0268401218305437
Yang, Y., Sun, Y., Kay, S., Yang, Q.: Defending online reputation systems against collaborative unfair raters through signal modeling and trust. In: SAC ’09: Proceedings of the 2009 ACM Symposium on Applied Computing, pp. 1308–1315 (2009). https://doi.org/10.1145/1529282.1529575
Acknowledgements
This work is supported by the Foundation of Jiangxi Educational Committee under Grant No. GJJ210338, the National Natural Science Foundation of China (NSFC) under Grant No. 61962026, the National Natural Science Key Foundation of China grant No. 61832014 and No. 62032016, the Natural Science Foundation of Jiangxi Province under Grant No. 20192ACBL21031.
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Xiao, J., Zeng, J., Miao, X., Cao, Y., Zhao, J., Feng, Z. (2023). USDSE: A Novel Method to Improve Service Reputation Based on Double-Side Evaluation. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13657. Springer, Cham. https://doi.org/10.1007/978-3-031-20102-8_37
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