Promoting Recommendations: An Attack on Collaborative Filtering
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
An Evaluation of Neighbourhood Formation on the Performance of Collaborative Filtering
Artificial Intelligence Review
Classification features for attack detection in collaborative recommender systems
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Lies and propaganda: detecting spam users in collaborative filtering
Proceedings of the 12th international conference on Intelligent user interfaces
The influence limiter: provably manipulation-resistant recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Attack resistant collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised strategies for shilling detection and robust collaborative filtering
User Modeling and User-Adapted Interaction
Model-based collaborative filtering as a defense against profile injection attacks
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Recommender systems: attack types and strategies
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Proceedings of the third ACM conference on Recommender systems
Robust Collaborative Recommendation by Least Trimmed Squares Matrix Factorization
ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Robust Sybil attack defense with information level in online Recommender Systems
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
The possibility of designing user rating profiles to deliberately and maliciously manipulate the recommendation output of a collaborative filtering system was first raised in 2002. One scenario proposed was that an author, motivated to increase recommendations of his book, might create a set of false profiles that rate the book highly, in an effort to artificially promote the ratings given by the system to genuine users. Several attack models have been proposed and the performance of these attacks in terms of influencing the system predictions has been evaluated for a number of memory-based and model-based collaborative filtering algorithms. Moreover, strategies have been proposed to enhance the robustness of existing algorithms and new algorithms have been proposed with built-in attack resistance. This tutorial will review the work that has taken place in the last decade on robustness of recommendation algorithms and seek to examine the question of the importance of robustness in future research.