论文标题
一个强大的基于声誉的小组排名系统及其对贿赂的抵抗力
A Robust Reputation-based Group Ranking System and its Resistance to Bribery
论文作者
论文摘要
在线评论和意见的传播及其对人们的行为和决策的影响不断增长，增强了从此数据洪水中提取有意义信息的兴趣。因此，产品和服务的众包评级在商业和政府中发挥了关键作用。当前的最新解决方案将项目平均表达为项目的平均值，因此对用户缺乏个性化，并接触攻击和垃圾邮件/虚假用户。将这些评分用于具有类似偏好的组用户，可能对用户提供反映其偏好并克服这些漏洞的项目。在本文中，我们提出了一个新的基于声誉的排名系统，利用多部分等级子网络，该系统使用三个措施以其相似性来促进用户，其中两个基于Kolmogorov的复杂性。我们还研究了其对贿赂的抵抗力以及如何设计最佳贿赂策略。我们的系统是新颖的，因为它通过（可能）为不同的用户组为同一项目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效率。通过对合成和真实数据进行测试，我们看到它可以与垃圾邮件/虚假用户更好，比最新的方法更强大。同样，通过聚类用户，与双方案例相比，贿赂在提议的多方排名系统中的效果变暗。
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.
