Shilling Attacks Detection in Recommender Systems Based on Target Item Analysis

Recommender systems are highly vulnerable to shilling attacks, both by individuals and groups. Attackers who introduce biased ratings in order to affect recommendations, have been shown to negatively affect collaborative filtering (CF) algorithms. Previous research focuses only on the differences be...

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Detalles Bibliográficos
Autores principales: Zhou, Wei, Wen, Junhao, Koh, Yun Sing, Xiong, Qingyu, Gao, Min, Dobbie, Gillian, Alam, Shafiq
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4519300/
https://www.ncbi.nlm.nih.gov/pubmed/26222882
http://dx.doi.org/10.1371/journal.pone.0130968

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4519300/
https://www.ncbi.nlm.nih.gov/pubmed/26222882
http://dx.doi.org/10.1371/journal.pone.0130968

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