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For: Cai H, Zhang F. Detecting shilling attacks in recommender systems based on analysis of user rating behavior. Knowl Based Syst 2019;177:22-43. [DOI: 10.1016/j.knosys.2019.04.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Number Cited by Other Article(s)
1
Detecting shilling groups in online recommender systems based on graph convolutional network. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.103031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
2
Himeur Y, Sohail SS, Bensaali F, Amira A, Alazab M. Latest trends of security and privacy in recommender systems: A comprehensive review and future perspectives. Comput Secur 2022. [DOI: 10.1016/j.cose.2022.102746] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
3
Wang Z, Gao M, Li J, Zhang J, Zhong J. Gray-Box Shilling Attack: An Adversarial Learning Approach. ACM T INTEL SYST TEC 2022. [DOI: 10.1145/3512352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
4
Bawack RE, Wamba SF, Carillo KDA, Akter S. Artificial intelligence in E-Commerce: a bibliometric study and literature review. ELECTRONIC MARKETS 2022;32:297-338. [PMID: 35600916 PMCID: PMC8932684 DOI: 10.1007/s12525-022-00537-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 02/22/2022] [Indexed: 06/15/2023]
5
TNE: A general time-aware network representation learning framework for temporal applications. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.108050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
6
Wang J, Gao M, Wang Z, Lin C, Zhou W, Wen J. Ada: Adversarial learning based data augmentation for malicious users detection. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108414] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
7
Li H, Gao M, Zhou F, Wang Y, Fan Q, Yang L. Fusing hypergraph spectral features for shilling attack detection. JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2021. [DOI: 10.1016/j.jisa.2021.103051] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
8
Detection of shilling attack in recommender system for YouTube video statistics using machine learning techniques. Soft comput 2021. [DOI: 10.1007/s00500-021-05586-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
9
Detecting Shilling Attacks Using Hybrid Deep Learning Models. Symmetry (Basel) 2020. [DOI: 10.3390/sym12111805] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]  Open
10
Graph embedding-based approach for detecting group shilling attacks in collaborative recommender systems. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.105984] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
11
Suchacka G, Iwański J. Identifying legitimate Web users and bots with different traffic profiles — an Information Bottleneck approach. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.105875] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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