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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...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2015
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| Materias: | |
| 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 |