Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | ACM SIGIR 2008 |
| Untertitel | 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Proceedings |
| Herausgeber (Verlag) | Association for Computing Machinery (ACM) |
| Seiten | 75-82 |
| Seitenumfang | 8 |
| ISBN (Print) | 9781605581644 |
| Publikationsstatus | Veröffentlicht - 20 Juli 2008 |
| Veranstaltung | 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM SIGIR 2008 - Singapore, Singapur Dauer: 20 Juli 2008 → 24 Juli 2008 |
Publikationsreihe
| Name | ACM SIGIR 2008 - 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Proceedings |
|---|
Abstract
The widespread deployment of recommender systems has lead to user feedback of varying quality. While some users faithfully express their true opinion, many provide noisy ratings which can be detrimental to the quality of the generated recommendations. The presence of noise can violate modeling assumptions and may thus lead to instabilities in estimation and prediction. Even worse, malicious users can deliberately insert attack profiles in an attempt to bias the recommender system to their benefit. While previous research has attempted to study the robustness of various existing Collaborative Filtering (CF) approaches, this remains an unsolved problem. Approaches such as Neighbor Selection algorithms, Association Rules and Robust Matrix Factorization have produced unsatisfactory results. This work describes a new collaborative algorithm based on SVD which is accurate as well as highly stable to shilling. This algorithm exploits previously established SVD based shilling detection algorithms, and combines it with SVD based-CF. Experimental results show a much diminished effect of all kinds of shilling attacks. This work also offers significant improvement over previous Robust Collaborative Filtering frameworks.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Information systems
- Informatik (insg.)
- Software
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
ACM SIGIR 2008: 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Proceedings. Association for Computing Machinery (ACM), 2008. S. 75-82 (ACM SIGIR 2008 - 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Proceedings).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Attack resistant collaborative filtering
AU - Mehta, Bhaskar
AU - Nejdl, Wolfgang
PY - 2008/7/20
Y1 - 2008/7/20
N2 - The widespread deployment of recommender systems has lead to user feedback of varying quality. While some users faithfully express their true opinion, many provide noisy ratings which can be detrimental to the quality of the generated recommendations. The presence of noise can violate modeling assumptions and may thus lead to instabilities in estimation and prediction. Even worse, malicious users can deliberately insert attack profiles in an attempt to bias the recommender system to their benefit. While previous research has attempted to study the robustness of various existing Collaborative Filtering (CF) approaches, this remains an unsolved problem. Approaches such as Neighbor Selection algorithms, Association Rules and Robust Matrix Factorization have produced unsatisfactory results. This work describes a new collaborative algorithm based on SVD which is accurate as well as highly stable to shilling. This algorithm exploits previously established SVD based shilling detection algorithms, and combines it with SVD based-CF. Experimental results show a much diminished effect of all kinds of shilling attacks. This work also offers significant improvement over previous Robust Collaborative Filtering frameworks.
AB - The widespread deployment of recommender systems has lead to user feedback of varying quality. While some users faithfully express their true opinion, many provide noisy ratings which can be detrimental to the quality of the generated recommendations. The presence of noise can violate modeling assumptions and may thus lead to instabilities in estimation and prediction. Even worse, malicious users can deliberately insert attack profiles in an attempt to bias the recommender system to their benefit. While previous research has attempted to study the robustness of various existing Collaborative Filtering (CF) approaches, this remains an unsolved problem. Approaches such as Neighbor Selection algorithms, Association Rules and Robust Matrix Factorization have produced unsatisfactory results. This work describes a new collaborative algorithm based on SVD which is accurate as well as highly stable to shilling. This algorithm exploits previously established SVD based shilling detection algorithms, and combines it with SVD based-CF. Experimental results show a much diminished effect of all kinds of shilling attacks. This work also offers significant improvement over previous Robust Collaborative Filtering frameworks.
KW - Collaborative filtering
KW - Recommendation algorithm
KW - Shilling
KW - SVD
UR - http://www.scopus.com/inward/record.url?scp=57349135767&partnerID=8YFLogxK
U2 - 10.1145/1390334.1390350
DO - 10.1145/1390334.1390350
M3 - Conference contribution
AN - SCOPUS:57349135767
SN - 9781605581644
T3 - ACM SIGIR 2008 - 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Proceedings
SP - 75
EP - 82
BT - ACM SIGIR 2008
PB - Association for Computing Machinery (ACM)
T2 - 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM SIGIR 2008
Y2 - 20 July 2008 through 24 July 2008
ER -