基于声誉的鲁棒群体排名系统及其抗贿赂性
A Robust Reputation-based Group Ranking System and its Resistance to Bribery
摘要：
在线评论和意见的传播及其对人们行为和决策的日益增长的影响，提高了从这些数据洪流中提取有意义信息的兴趣。因此，众包的产品和服务评级在企业和政府中发挥了关键作用。当前最先进的解决方案利用针对项目表达的评级的平均值来对项目进行排名，结果是用户缺乏个性化，并且暴露于攻击和垃圾邮件/虚假用户。使用这些评级对具有相似偏好的用户进行分组可能有助于向用户呈现反映其偏好的项目并克服这些漏洞。在本文中，我们提出了一个新的基于声誉的排名系统，利用多部评级子网络，聚类用户的相似性，使用三种措施，其中两个基于柯尔莫哥洛夫复杂性。我们还研究了它对贿赂的抵抗力以及如何设计最佳的贿赂策略。我们的系统的新颖之处在于，它通过（可能）为不同用户群体的同一项分配不同的排名来反映偏好的多样性。我们证明了系统的收敛性和有效性。通过在合成数据和真实数据上进行测试，我们发现它能更好地应对垃圾邮件/虚假用户，比最先进的方法更能抵御攻击。此外，通过对用户进行聚类，与两方情况相比，在所提出的多方排名系统中贿赂的影响是暗淡的。
英文摘要：
The spread of online reviews and opinions and its growing influence on people's behavior and decisions, boosted the interest to extract meaningful information from this data deluge. Hence, crowdsourced ratings of products and services gained a critical role in business and governments. Current state-of-the-art solutions rank the items with an average of the ratings expressed for an item, with a consequent lack of personalization for the users, and the exposure to attacks and spamming/spurious users. Using these ratings to group users with similar preferences might be useful to present users with items that reflect their preferences and overcome those vulnerabilities. In this paper, we propose a new reputation-based ranking system, utilizing multipartite rating subnetworks, which clusters users by their similarities using three measures, two of them based on Kolmogorov complexity. We also study its resistance to bribery and how to design optimal bribing strategies. Our system is novel in that it reflects the diversity of preferences by (possibly) assigning distinct rankings to the same item, for different groups of users. We prove the convergence and efficiency of the system. By testing it on synthetic and real data, we see that it copes better with spamming/spurious users, being more robust to attacks than state-of-the-art approaches. Also, by clustering users, the effect of bribery in the proposed multipartite ranking system is dimmed, comparing to the bipartite case.
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