Comparing MLP, SVM and KNN for predicting trust between users in Facebook

被引:0
|
作者
Khadangi, Ehsan [1 ]
Bagheri, Alireza [1 ]
机构
[1] Amirkabir Univ Technol, Dept Comp Engn, Tehran, Iran
来源
PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON COMPUTER AND KNOWLEDGE ENGINEERING (ICCKE 2013) | 2013年
关键词
Trust Prediction; Online Social Network; Facebook; k-nearest neighbor; Support Vector Machine; Multilayer Perceptron;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Trust is one of the most important elements in social relationships. This importance becomes more conspicuous when the person's interactions take place in a dynamic environment with high uncertainty. Online social networks are websites where information propagates quickly and once the user has entered, they face a huge amount of information all of which they cannot assess. Therefore, it should be possible to rank the presented information based on issues such as trust. Trust prediction in social networks which do not support implicit rating mechanisms is a challenging problem. It is shown in this paper how it is possible to measure the trust between users in Facebook based on the information of their interactions and profile. For this, a dataset comprising interactions, profile information, and trust amount between users was collected. Then after the preprocess of this data set, three methods MLP1, KNN2 and SVM3 were used for trust prediction. Using 10-fold cross validation, we observed that MLP can measure trust with 83% accuracy. The accuracy of the best KNN model, with k=12, and SVM were 73% and 71% respectively. Recall, precision and area under the ROC curve of SVM and KNN were also significantly lower than MLP. According to these results, MLP can measure the trust between users with high accuracy based on the information of their interactions and profile.
引用
收藏
页码:466 / 470
页数:5
相关论文
共 50 条
  • [41] Predicting users' behavior: Gender and age as interactive antecedents of students' Facebook use for research data collection
    Petters, Janet Sunday
    Owan, Valentine Joseph
    Okpa, Ovat Egbe
    Idika, Delight Omoji
    Ojini, Richard Ayuh
    Ntamu, Blessing Agbo
    Robert, Augustine Igwe
    Owan, Mercy Valentine
    Asu-Okang, Stella
    Essien, Victor Eyo
    ONLINE JOURNAL OF COMMUNICATION AND MEDIA TECHNOLOGIES, 2024, 14 (01):
  • [42] Comparative Analysis between SVM & KNN Classifier for EMG Signal Classification on Elementary Time Domain Features
    Paul, Yogesh
    Goyal, Vibha
    Jaswal, Ram Avtar
    PROCEEDINGS OF 4TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMPUTING AND CONTROL (ISPCC 2K17), 2017, : 169 - 175
  • [43] A Reliable Point of Interest Recommendation based on Trust Relevancy between Users
    R. Logesh
    V. Subramaniyaswamy
    Wireless Personal Communications, 2017, 97 : 2751 - 2780
  • [44] Investigating the Relationship Between Trust and Sentiment Agreement in Arab Twitter Users
    Alowisheq, Areeb
    Alrajebah, Nora
    Alrumikhani, Asma
    Al-Shamrani, Ghadeer
    Shaabi, Maha
    Al-Nufaisi, Muneera
    Alnasser, Ahad
    Alhumoud, Sarah
    SOCIAL COMPUTING AND SOCIAL MEDIA: APPLICATIONS AND ANALYTICS, SCSM 2017, PT II, 2017, 10283 : 236 - 245
  • [45] Building Trust Between Users and Telecommunications Data Driven Virtual Assistants
    Perez Garcia, Marta
    Saffon Lopez, Sarita
    ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2018, 2018, 519 : 628 - 637
  • [46] A Reliable Point of Interest Recommendation based on Trust Relevancy between Users
    Logesh, R.
    Subramaniyaswamy, V.
    WIRELESS PERSONAL COMMUNICATIONS, 2017, 97 (02) : 2751 - 2780
  • [47] Better Trust Between Users in Sharing Economy Platforms Completed Research
    Alsamani, Badr
    AMCIS 2018 PROCEEDINGS, 2018,
  • [48] Research on the influencing factors and the differences between the initial trust and continuous trust of online health community users
    Wang, Zongrun
    Liang, Lin
    Liu, Xin
    Liao, Minglong
    NEURAL COMPUTING & APPLICATIONS, 2024, 36 (17): : 9849 - 9874
  • [49] Comparing LASSO and random forest models for predicting neurological dysfunction among fluoroquinolone users
    Ellis, Darcy E.
    Hubbard, Rebecca A.
    Willis, Allison W.
    Zuppa, Athena F.
    Zaoutis, Theoklis E.
    Hennessy, Sean
    PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, 2022, 31 (04) : 393 - 403
  • [50] Predicting Facebook-Users' Personality based on Status and Linguistic Features via Flexible Regression Analysis Techniques
    Howlader, Prantik
    Pal, Kuntal Kumar
    Cuzzocrea, Alfredo
    Kumar, S. D. Madhu
    33RD ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, 2018, : 339 - 345