JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2019, Vol. 54 ›› Issue (1): 53-59.doi: 10.6040/j.issn.1671-9352.3.2018.007

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A user behavior trust evaluation combined with sliding window and recommended trust

Ze-nan WU1(),Li-qin TIAN1,2,*(),Zhi-gang WANG1   

  1. 1. School of Computer Science and Technology, Qinghai Normal University, Xining 810008, Qinghai, China
    2. School of Computer Science, North China Institute of Science and Technology, Beijing 101601, China
  • Received:2018-06-26 Online:2019-01-20 Published:2019-01-23
  • Contact: Li-qin TIAN E-mail:18511465255@163.com;tianliqin@ncist.edu.cn
  • Supported by:
    国家自然科学基金资助项目(61472137);国家重点研发计划资助项目(2017YFC0804108);青海省重点实验室、重点研发项目(2017-ZJ-752);青海省重点实验室、重点研发项目(2017-ZJ-Y21);青海省重点实验室、重点研发项目(2016-SF-130);河北省物联网监控工程技术研究中心项目(3142016020)

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Abstract:

A new evaluation method of user behavior trust by adopting social characteristics of trust and computer requirements for assessment is proposed. This method improves the sliding window, and makes reasonable calculation of user behavior evidence through the initial setting of the sliding window, the size of the window, the interval of the sliding and the update of the window content, so as to improve the scalability of the user behavior evaluation. Meanwhile, combined with the recommended trust mechanism to further enhance the credibility of user behavior assessment. Finally, the trust value of the user behavior obtained is compared with the default trust evaluation standard of the system to obtain the user trust level. Finally, through the experimental data analysis, the evaluation method has the characteristics of finer granularity, the evaluation object is the user rather than the service provider, and the evaluation result is more in line with the actual situation.

Key words: sliding window, recommendation trust, user behavior trust assessment, credibility and scalability

CLC Number: 

  • TP309

Fig.1

Sliding window model"

Table 1

Trust rank list"

[0, 0.2] [0.2, 0.4] [0.4, 0.6] [0.6, 0.8] [0.8, 1.0]
U_danger U_strange U_suspicious U_ordinary U_credible

Fig.2

Multipath recommendation trust model"

Table 2

Value change of user trust"

项目 信任值
初始 1周 2周 3周 1个月
有时间衰减 0.89 0.52 0.45 0.39 0.28
无时间衰减 0.89 0.89 0.89 0.89 0.89

Table 3

Trust value "slow rise" control data analysis"

交互次数 信任值
无滑动窗口设置 有滑动窗口最小次数设置 滑动窗口+推荐信任算法
1 0.30 0.30 0.30
2 0.65 0.41 0.35
3 0.79 0.52 0.42
4 0.81 0.63 0.51
5 0.95 0.71 0.58
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