An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
Proceedings of the 10th international conference on Intelligent user interfaces
Preventing shilling attacks in online recommender systems
Proceedings of the 7th annual ACM international workshop on Web information and data management
Is trust robust?: an analysis of trust-based recommendation
Proceedings of the 11th international conference on Intelligent user interfaces
Classification features for attack detection in collaborative recommender systems
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting Profile Injection Attacks in Collaborative Recommender Systems
CEC-EEE '06 Proceedings of the The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
Model-based collaborative filtering as a defense against profile injection attacks
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Collaborative filtering recommender systems
The adaptive web
Using data mining and recommender systems to scale up the requirements process
Proceedings of the 2nd international workshop on Ultra-large-scale software-intensive systems
Recommendation method that considers the context of product purchases
WSEAS Transactions on Information Science and Applications
Dependable filtering: Philosophy and realizations
ACM Transactions on Information Systems (TOIS)
People recommendation based on aggregated bidirectional intentions in social network site
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Secure personalized recommendation system for mobile user
ICISC'10 Proceedings of the 13th international conference on Information security and cryptology
A hybrid approach for personalized recommendation of news on the Web
Expert Systems with Applications: An International Journal
A user trust-based collaborative filtering recommendation algorithm
ICICS'09 Proceedings of the 11th international conference on Information and Communications Security
Robustness analysis of privacy-preserving model-based recommendation schemes
Expert Systems with Applications: An International Journal
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Preserving user trust in recommender system depends on the perception of the system as objective, unbiased, and accurate. However, publicly accessible user-adaptive systems such as collaborative recommender systems present a security problem. Attackers, closely resembling ordinary users, might introduce biased profiles to force the system to adapt in a manner advantageous to them. The authors discuss some of the major issues in building secure recommender systems, including some of the most effective attacks and their impact on various recommendation algorithms. Approaches for responding to these attacks range from algorithmic approaches to designing more robust recommenders, to effective methods for detecting and eliminating suspect profiles.