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Shilling attack detection for recommender systems based on credibility of group users and rating time series
Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainabi...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2018
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5942815/ https://www.ncbi.nlm.nih.gov/pubmed/29742134 http://dx.doi.org/10.1371/journal.pone.0196533 |