Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | WIDM 2005 |
| Untertitel | Proceedings of the 7th ACM International Workshop on Web Information and Data Management, Co-located with CIKM 2005 |
| Herausgeber (Verlag) | Association for Computing Machinery (ACM) |
| Seiten | 67-74 |
| Seitenumfang | 8 |
| ISBN (Print) | 1595931945, 9781595931948 |
| Publikationsstatus | Veröffentlicht - 4 Nov. 2005 |
| Veranstaltung | CIKM'05: 14th ACM International Conference on Information and Knowledge Management - Bremen, Deutschland Dauer: 31 Okt. 2005 → 5 Nov. 2005 |
Publikationsreihe
| Name | Proceedings of the Interntational Workshop on Web Information and Data Management WIDM |
|---|
Abstract
Collaborative filtering techniques have been successfully employed in recommender systems in order to help users deal with information overload by making high quality personalized recommendations. However, such systems have been shown to be vulnerable to attacks in which malicious users with carefully chosen profiles are inserted into the system in order to push the predictions of some targeted items. In this paper we propose several metrics for analyzing rating patterns of malicious users and evaluate their potential for detecting such shilling attacks. Building upon these results, we propose and evaluate an algorithm for protecting rec-ommender systems against shilling attacks. The algorithm can be employed for monitoring user ratings and removing shilling attacker profiles from the process of computing recommendations, thus maintaining the high quality of the recommendations.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Information systems
- Informatik (insg.)
- Software
Zitieren
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- Harvard
- Apa
- Vancouver
- BibTex
- RIS
WIDM 2005: Proceedings of the 7th ACM International Workshop on Web Information and Data Management, Co-located with CIKM 2005. Association for Computing Machinery (ACM), 2005. S. 67-74 (Proceedings of the Interntational Workshop on Web Information and Data Management WIDM).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Preventing shilling attacks in online recommender systems
AU - Chirita, Paul Alexandru
AU - Nejdl, Wolfgang
AU - Zamfir, Cristian
PY - 2005/11/4
Y1 - 2005/11/4
N2 - Collaborative filtering techniques have been successfully employed in recommender systems in order to help users deal with information overload by making high quality personalized recommendations. However, such systems have been shown to be vulnerable to attacks in which malicious users with carefully chosen profiles are inserted into the system in order to push the predictions of some targeted items. In this paper we propose several metrics for analyzing rating patterns of malicious users and evaluate their potential for detecting such shilling attacks. Building upon these results, we propose and evaluate an algorithm for protecting rec-ommender systems against shilling attacks. The algorithm can be employed for monitoring user ratings and removing shilling attacker profiles from the process of computing recommendations, thus maintaining the high quality of the recommendations.
AB - Collaborative filtering techniques have been successfully employed in recommender systems in order to help users deal with information overload by making high quality personalized recommendations. However, such systems have been shown to be vulnerable to attacks in which malicious users with carefully chosen profiles are inserted into the system in order to push the predictions of some targeted items. In this paper we propose several metrics for analyzing rating patterns of malicious users and evaluate their potential for detecting such shilling attacks. Building upon these results, we propose and evaluate an algorithm for protecting rec-ommender systems against shilling attacks. The algorithm can be employed for monitoring user ratings and removing shilling attacker profiles from the process of computing recommendations, thus maintaining the high quality of the recommendations.
KW - Collaborative filtering
KW - Recommender systems
KW - Shilling attacks
KW - Web applications
UR - http://www.scopus.com/inward/record.url?scp=84876544258&partnerID=8YFLogxK
U2 - 10.1145/1097047.1097061
DO - 10.1145/1097047.1097061
M3 - Conference contribution
AN - SCOPUS:84876544258
SN - 1595931945
SN - 9781595931948
T3 - Proceedings of the Interntational Workshop on Web Information and Data Management WIDM
SP - 67
EP - 74
BT - WIDM 2005
PB - Association for Computing Machinery (ACM)
T2 - CIKM'05
Y2 - 31 October 2005 through 5 November 2005
ER -