Enhancing the robustness of recommender systems against spammers.
PLoS One
; 13(11): e0206458, 2018.
Article
em En
| MEDLINE
| ID: mdl-30383766
ABSTRACT
The accuracy and diversity of recommendation algorithms have always been the research hotspot of recommender systems. A good recommender system should not only have high accuracy and diversity, but also have adequate robustness against spammer attacks. However, the issue of recommendation robustness has received relatively little attention in the literature. In this paper, we systematically study the influences of different spammer behaviors on the recommendation results in various recommendation algorithms. We further propose an improved algorithm by incorporating the inner-similarity of user's purchased items in the classic KNN approach. The new algorithm effectively enhances the robustness against spammer attacks and thus outperforms traditional algorithms in recommendation accuracy and diversity when spammers exist in the online commercial systems.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Software
/
Sistemas On-Line
/
Segurança Computacional
/
Guias como Assunto
/
Comportamento do Consumidor
/
Internet
Tipo de estudo:
Guideline
Limite:
Humans
Idioma:
En
Revista:
PLoS One
Ano de publicação:
2018
Tipo de documento:
Article