GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Recommender systems: attack types and strategies
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Dependable filtering: Philosophy and realizations
ACM Transactions on Information Systems (TOIS)
Content-driven detection of campaigns in social media
Proceedings of the 20th ACM international conference on Information and knowledge management
A hybrid approach for personalized recommendation of news on the Web
Expert Systems with Applications: An International Journal
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Campaign extraction from social media
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
| Hi-index | 0.00 |
Collaborative recommender systems are vulnerable to attacks that seek to manipulate recommendations made for target items. The authors examine such attacks from a cost perspective, focusing on the effect that attack size—in terms of the number of ratings inserted during an attack—has on attack success. They present a cost-benefit analysis that shows that attackers can realize profits, even when financial costs are imposed on the insertion of ratings.