An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
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)
A trust-enhanced recommender system application: Moleskiing
Proceedings of the 2005 ACM symposium on Applied computing
Preventing shilling attacks in online recommender systems
Proceedings of the 7th annual ACM international workshop on Web information and data management
Classification features for attack detection in collaborative recommender systems
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
International Journal of Electronic Commerce
Mining influence in recommender systems
Mining influence in recommender systems
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Robustness of collaborative recommendation based on association rule mining
Proceedings of the 2007 ACM conference on Recommender systems
Unsupervised retrieval of attack profiles in collaborative recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Learning preferences of new users in recommender systems: an information theoretic approach
ACM SIGKDD Explorations Newsletter
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
A hybrid of sequential rules and collaborative filtering for product recommendation
Information Sciences: an International Journal
Information Sciences: an International Journal
Selecting a small number of products for effective user profiling in collaborative filtering
Expert Systems with Applications: An International Journal
Average Shilling Attack against Trust-Based Recommender Systems
ICIII '09 Proceedings of the 2009 International Conference on Information Management, Innovation Management and Industrial Engineering - Volume 04
ACM Transactions on Computer-Human Interaction (TOCHI)
Efficient k-nearest neighbor searching in nonordered discrete data spaces
ACM Transactions on Information Systems (TOIS)
Improved trust-aware recommender system using small-worldness of trust networks
Knowledge-Based Systems
Use of social network information to enhance collaborative filtering performance
Expert Systems with Applications: An International Journal
On the stability of recommendation algorithms
Proceedings of the fourth ACM conference on Recommender systems
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
User comments for news recommendation in forum-based social media
Information Sciences: an International Journal
Analysis of Love-Hate Shilling Attack Against E-commerce Recommender System
ISME '10 Proceedings of the 2010 International Conference of Information Science and Management Engineering - Volume 01
Social manipulation of online recommender systems
SocInfo'10 Proceedings of the Second international conference on Social informatics
Dynamic time warping constraint learning for large margin nearest neighbor classification
Information Sciences: an International Journal
Analysis and detection of segment-focused attacks against collaborative recommendation
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
Who are the most influential users in a recommender system?
Proceedings of the 13th International Conference on Electronic Commerce
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Collaborative filtering (CF) is a popular method for personalizing product recommendations for e-commerce applications. In order to recommend a product to a user and predict that user's preference, CF utilizes product evaluation ratings of like-minded users. The process of finding like-minded users forms a social network among all users and each link between two users represents an implicit connection between them. Users having more connections with others are the most influential users. Attacking recommender systems is a new issue for these systems. Here, an attacker tries to manipulate a recommender system in order to change the recommendation output according to her wish. If an attacker succeeds, her profile is used over and over again by the recommender system, making her an influential user. In this study, we applied the established attack detection methods to the influential users, instead of the whole user set, to improve their attack detection performance. Experiments were conducted using the same settings previously used to test the established methods. The results showed that the proposed influence-based method had better detection performance and improved the stability of a recommender system for most attack scenarios. It performed considerably better than established detection methods for attacks that inserted low numbers of attack profiles (20---25 %).