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Neural ranking models have achieved remarkable progress and are now widely deployed in real-world applications such as Retrieval-Augmented Generation (RAG). However, like other neural architectures, they remain vulnerable to adversarial…

密码学与安全 · 计算机科学 2025-12-30 Jiawei Liu , Zhuo Chen , Rui Zhu , Miaokun Chen , Yuyang Gong , Wei Lu , Xiaofeng Wang

Finding pertinent information is not limited to search engines. Online communities can amplify the influence of a small number of power users for the benefit of all other users. Users' information foraging in depth and breadth can be…

物理与社会 · 物理学 2011-07-01 Linyuan Lu , Yi-Cheng Zhang , Chi Ho Yeung , Tao Zhou

Ranking systems form the basis for online search engines and recommendation services. They process large collections of items, for instance web pages or e-commerce products, and present the user with a small ordered selection. The goal of a…

信息检索 · 计算机科学 2020-12-14 Harrie Oosterhuis

Pairwise ranking systems based on Maximum Likelihood Estimation (MLE), such as the Bradley-Terry model, are widely used to aggregate preferences from pairwise comparisons. However, their robustness under strategic data manipulation remains…

机器学习 · 计算机科学 2026-04-21 Junyi Yao , Zihao Zheng , Jiayu Long

Rank aggregation with pairwise comparisons has shown promising results in elections, sports competitions, recommendations, and information retrieval. However, little attention has been paid to the security issue of such algorithms, in…

机器学习 · 计算机科学 2022-09-14 Ke Ma , Qianqian Xu , Jinshan Zeng , Guorong Li , Xiaochun Cao , Qingming Huang

Search engines could consistently favor certain values over the others, which is considered as biased due to the built-in infrastructures. Many studies have been dedicated to detect, control, and mitigate the impacts of the biases from the…

信息检索 · 计算机科学 2021-05-10 Bin Han , Chirag Shah , Daniel Saelid

The unevenness importance of criminal activities in the onion domains of the Tor Darknet and the different levels of their appeal to the end-user make them tangled to measure their influence. To this end, this paper presents a novel…

机器学习 · 计算机科学 2019-10-08 Mhd Wesam Al-Nabki , Eduardo Fidalgo , Enrique Alegre , Deisy Chaves

Rankings have become the primary interface in two-sided online markets. Many have noted that the rankings not only affect the satisfaction of the users (e.g., customers, listeners, employers, travelers), but that the position in the ranking…

信息检索 · 计算机科学 2022-08-31 Yuta Saito , Thorsten Joachims

We propose a method for evaluating the robustness of widely used LLM ranking systems -- variants of a Bradley--Terry model -- to dropping a worst-case very small fraction of preference data. Our approach is computationally fast and easy to…

机器学习 · 统计学 2026-03-06 Jenny Y. Huang , Yunyi Shen , Dennis Wei , Tamara Broderick

Ensuring the early detection of important social network users is a challenging task. Some peer ranking services are now well established, such as PeerIndex, Klout, or Kred. Their function is to rank users according to their influence. This…

社会与信息网络 · 计算机科学 2016-10-10 Erwan Le Merrer , Gilles Trédan

The paper introduces a novel mechanism for approximating traffic of the academic sites (universities and research institutes) in the World Wide Web based on Alexa rankings. Firstly we introduce and discuss new method for calculating score…

数字图书馆 · 计算机科学 2020-04-14 Bahram Kalhor , Mohammad Reza Ghane , Alireza Nikravanshalmani

Online services pervasively employ manipulative designs (i.e., dark patterns) to influence users to purchase goods and subscriptions, spend more time on-site, or mindlessly accept the harvesting of their personal data. To protect users from…

Sparse PCA is a widely used technique for high-dimensional data analysis. In this paper, we propose a new method called low-rank principal eigenmatrix analysis. Different from sparse PCA, the dominant eigenvectors are allowed to be dense…

机器学习 · 统计学 2019-04-30 Krishna Balasubramanian , Elynn Y. Chen , Jianqing Fan , Xiang Wu

Ranking on large-scale graphs plays a fundamental role in many high-impact application domains, ranging from information retrieval, recommender systems, sports team management, biology to neuroscience and many more. PageRank, together with…

社会与信息网络 · 计算机科学 2018-11-27 Jian Kang , Meijia Wang , Nan Cao , Yinglong Xia , Wei Fan , Hanghang Tong

In the search engine of Google, the PageRank algorithm plays a crucial role in ranking the search results. The algorithm quantifies the importance of each web page based on the link structure of the web. We first provide an overview of the…

系统与控制 · 计算机科学 2012-03-30 Hideaki Ishii , Roberto Tempo

The PageRank algorithm enables to rank the nodes of a network through a specific eigenvector of the Google matrix, using a damping parameter $\alpha \in ]0,1[$. Using extensive numerical simulations of large web networks, with a special…

信息检索 · 计算机科学 2011-11-04 K. M. Frahm , B. Georgeot , D. L. Shepelyansky

Large language models (LLMs) increasingly rank products, documents, and recommendations for user queries, which makes manipulating these rankings a growing concern for fairness and information integrity. Research on generative engine…

密码学与安全 · 计算机科学 2026-05-29 Ojas Nimase , Zhe Chen , Gengpei Qi , Yue Zhao , Xiyang Hu

Since the advent of the Internet, quantifying the relative importance of web pages is at the core of search engine methods. According to one algorithm, PageRank, the worldwide web structure is represented by the Google matrix, whose…

无序系统与神经网络 · 物理学 2021-04-07 Kirill P. Kalinin , Natalia G. Berloff

Ranking functions such as PageRank assign numeric values (ranks) to nodes of graphs, most notably the web graph. Node rankings are an integral part of Internet search algorithms, since they can be used to order the results of queries.…

How to rank web pages, scientists and online resources has recently attracted increasing attention from both physicists and computer scientists. In this paper, we study the ranking problem of rating systems where users vote objects by…

信息检索 · 计算机科学 2010-01-14 Luo-Luo Jiang , Matus Medo , Joseph R. Wakeling , Yi-Cheng Zhang , Tao Zhou