A Reputation-Resilient Seller Ranking Mechanism for Online Marketplaces
Keywords:
Reputation Systems, Seller Ranking, Online Marketplaces, Adversarial Robustness, Trust Modeling, E-Commerce Security, Algorithmic Fairness, Machine LearningAbstract
Online marketplaces increasingly depend on algorithmic seller ranking systems to determine product visibility, consumer trust, and revenue allocation. However, traditional reputation-based ranking mechanisms remain vulnerable to adversarial manipulation, including fake reviews, rating inflation, and coordinated ranking attacks. These vulnerabilities compromise ranking integrity, distort market competition, and weaken consumer confidence. To address these challenges, this paper proposes a Reputation-Resilient Seller Ranking Mechanism (RRSRM) that integrates adaptive trust modeling with adversarial risk adjustment. The proposed framework formulates seller reputation as a multi-dimensional weighted function and introduces a risk-sensitive penalty term to reduce the influence of suspicious behavioral signals. In addition, a robustness-aware optimization strategy balances historical trust indicators with contextual performance metrics to ensure stable and fair ranking outcomes. The experimental evaluation demonstrates improved robustness under adversarial conditions while maintaining competitive ranking quality and statistical consistency. The results indicate that embedding adversarial resilience directly into the reputation computation process significantly enhances system stability without compromising scalability. The proposed mechanism contributes a structured and mathematically grounded approach for strengthening trust, transparency, and resilience in AI-driven e-commerce ecosystems.
Downloads
References
S. A. Rajagukguk and D. Sofyan, "Detecting Sophisticated Fake Reviews on E-Commerce Platforms Using Adversarial Transformer Networks," Engineering, Technology & Applied Science Research, vol. 15, no. 6, pp. 29840-29845, 2025.
J. Reynolds, "eCommerce: a critical review," International Journal of Retail & Distribution Management, vol. 28, no. 10, pp. 417-444, 2000.
K. Ma et al., "Sequential manipulation against rank aggregation: theory and algorithm," IEEE transactions on pattern analysis and machine intelligence, vol. 46, no. 12, pp. 9353-9370, 2024.
N. Wang, Y. Huang, H. Cheng, J. Gesi, X. Wang, and V. Mittal, "Towards robustness analysis of e-commerce ranking system," in Companion Proceedings of the ACM Web Conference 2024, 2024, pp. 364-373.
H. Ramamoorthy, S. Gupta, and S. Sundaram, "Distributed online life-long learning (DOL3) for multi-agent trust and reputation assessment in E-commerce," arXiv preprint arXiv:2410.16529, 2024.
A. P. Mattathil, B. George, and T. L. Henthorne, "Trust as Behavioral Architecture: How E-Commerce Platforms Shape Consumer Judgment and Agency," Platforms, vol. 4, no. 1, p. 2, 2026.
Ö. Doğan and H. Karacan, "A blockchain-based e-commerce reputation system built with verifiable credentials," IEEE Access, vol. 11, pp. 47080-47097, 2023.
A. Bigdeli, N. Arabzadeh, E. Bagheri, and C. L. Clarke, "Adversarial Attacks against Neural Ranking Models via In-Context Learning," in Proceedings of the 2025 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, 2025, pp. 211-220.
J. Liu et al., "RobustMask: Certified Robustness against Adversarial Neural Ranking Attack via Randomized Masking," arXiv preprint arXiv:2512.23307, 2025.
S. Qiu, Q. Liu, S. Zhou, and C. Wu, "Review of artificial intelligence adversarial attack and defense technologies," Applied Sciences, vol. 9, no. 5, p. 909, 2019.
S. Olariu, R. Mukkamala, and M. Aljohani, "Towards trust and reputation as a service in a blockchain-based decentralized marketplace," arXiv preprint arXiv:2403.04779, 2024.
S. Rong, E. Hamdan, and A. E. Cetin, "Multi-resolution training improves robustness against adversarial attacks," Signal, Image and Video Processing, vol. 19, no. 6, p. 481, 2025.
Z. Awad, M. Zakaria, and R. Hassan, "An enhanced ensemble defense framework for boosting adversarial robustness of intrusion detection systems," Scientific Reports, vol. 15, no. 1, p. 14177, 2025.
F. Qian, W. Chen, H. Chen, J. Liu, S. Zhao, and Y. Zhang, "Building robust deep recommender systems: Utilizing a weighted adversarial noise propagation framework with robust fine-tuning modules," Knowledge-Based Systems, vol. 314, p. 113181, 2025.
