Details
| Original language | English |
|---|---|
| Title of host publication | WIDM 2005 |
| Subtitle of host publication | Proceedings of the 7th ACM International Workshop on Web Information and Data Management, Co-located with CIKM 2005 |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 67-74 |
| Number of pages | 8 |
| ISBN (print) | 1595931945, 9781595931948 |
| Publication status | Published - 4 Nov 2005 |
| Event | 7th ACM International Workshop on Web Information and Data Management, WIDM 2005, Held in Conjunction with the International Conference on Information and Knowledge Management, CIKM 2005 - Bremen, Germany Duration: 4 Nov 2005 → 4 Nov 2005 |
Publication series
| 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.
Keywords
- Collaborative filtering, Recommender systems, Shilling attacks, Web applications
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Software
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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. p. 67-74 (Proceedings of the Interntational Workshop on Web Information and Data Management WIDM).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › 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 - 7th ACM International Workshop on Web Information and Data Management, WIDM 2005, Held in Conjunction with the International Conference on Information and Knowledge Management, CIKM 2005
Y2 - 4 November 2005 through 4 November 2005
ER -