Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks  

作　　者：Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang 廖好;刘启鑫;黄泽成;陆克中;杨志豪;张翼成(National Engineering Laboratory on Big Data System Computing Technology,College of Computer Science and Software,Engineering,Shenzhen University,Shenzhen 518060,China;Guangdong Province Key Laboratory of Popular High Performance Computers,Shenzhen University,Shenzhen 518060,China;Guangdong Province Engineering Center of China-Made High Performance Data Computing System,College of Computer,Science and Software Engineering,Shenzhen University,Shenzhen 518060,China;Institute of Big Data Intelligent Management and Decision,Shenzhen University,Shenzhen 518060,China;Department of Science and Environmental Studies,The Education University of Hong Kong,Hong Kong 999077,China;Department of Physics,University of Fribourg,Fribourg 1700,Switzerland)

机构地区：[1]National Engineering Laboratory on Big Data System Computing Technology,College of Computer Science and Software,Engineering,Shenzhen University,Shenzhen 518060,China [2]Guangdong Province Key Laboratory of Popular High Performance Computers,Shenzhen University,Shenzhen 518060,China [3]Guangdong Province Engineering Center of China-Made High Performance Data Computing System,College of Computer,Science and Software Engineering,Shenzhen University,Shenzhen 518060,China [4]Institute of Big Data Intelligent Management and Decision,Shenzhen University,Shenzhen 518060,China [5]Department of Science and Environmental Studies,The Education University of Hong Kong,Hong Kong 999077,China [6]Department of Physics,University of Fribourg,Fribourg 1700,Switzerland

出　　处：《Journal of Computer Science & Technology》2022年第4期960-974,共15页计算机科学技术学报（英文版）

基　　金：This work was supported by the National Natural Science Foundation of China under Grant No.61803266;the Natural Science Foundation of Guangdong Province of China under Grant Nos.2019A1515011173 and 2019A1515011064;the Shenzhen Fundamental Research-General Project under Grant No.JCYJ20190808162601658;the Research Grants Council of the Hong Kong Special Administrative Region,China,under Grant Nos.GRF 18304316,GRF 18301217 and GRF 18301119;the Dean's Research Fund of the Faculty of Liberal Arts and Social Sciences,The Education University of Hong Kong,Hong Kong Special Administrative Region,China,under Grant No.FLASS/DRF 04418,and the CCF-Baidu Open Fund.

摘　　要：Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关 键 词：temporal network behavior dynamics reputation evaluation ranking algorithm 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]