Journal of Computer Science and Technology ›› 2022, Vol. 37 ›› Issue (4): 960-974.doi: 10.1007/s11390-021-0471-4

Special Issue: Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Regular Paper • Previous Articles     Next Articles

Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

Hao Liao1,2,3,4 (廖好), Senior Member, CCF, Qi-Xin Liu1,2,3 (刘启鑫), Ze-Cheng Huang1,2,3 (黄泽成), Ke-Zhong Lu1,2,3,* (陆克中), Chi Ho Yeung5,* (杨志豪), and Yi-Cheng Zhang6 (张翼成)         

  1. 1National Engineering Laboratory on Big Data System Computing Technology, College of Computer Science and Software, Engineering, Shenzhen University, Shenzhen 518060, China
    2Guangdong Province Key Laboratory of Popular High Performance Computers, Shenzhen University, Shenzhen 518060, China
    3Guangdong Province Engineering Center of China-Made High Performance Data Computing System, College of Computer, Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
    4Institute of Big Data Intelligent Management and Decision, Shenzhen University, Shenzhen 518060, China
    5Department of Science and Environmental Studies, The Education University of Hong Kong, Hong Kong 999077, China
    6Department of Physics, University of Fribourg, Fribourg 1700, Switzerland
  • Received:2020-03-27 Revised:2021-08-28 Accepted:2021-12-03 Online:2022-07-25 Published:2022-07-25
  • Contact: Ke-Zhong Lu, Chi Ho Yeung;
  • About author:Ke-Zhong Lu received his Bachelor's and Ph.D. degrees in computer science from the University of Science and Technology of China, Hefei, in 2001 and 2006, respectively. He is a professor of the College of Computer Science and Software Engineering, Shenzhen University, Shenzhen. His research concerns big data, parallel and distributed computing, and wireless sensor network. He is a vice dean of the College of Computer Science and Software Engineering at Shenzhen University, Shenzhen.
    Chi Ho Yeung received his Ph.D. degree in physics from the Hong Kong University of Science and Technology (HKUST), Hong Kong, in 2009. He then worked as a postdoctoral research fellow in University of Fribourg in Switzerland and Aston University in the United Kingdom for four years. He is currently an associate professor in the Department of Science and Environmental Studies, The Education University of Hong Kong, Hong Kong. His major research interests include statistical physics, transportation networks, complex and social networks, artificial intelligence, and STEM education.
  • Supported by:
    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.

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.

Key words: temporal network; behavior dynamics; reputation evaluation; ranking algorithm;

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