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
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Promoting Recommendations: An Attack on Collaborative Filtering
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
kNN CF: a temporal social network
Proceedings of the 2008 ACM conference on Recommender systems
A market-based approach to address the new item problem
Proceedings of the fifth ACM conference on Recommender systems
RecMax: exploiting recommender systems for fun and profit
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Power users, in a Collaborative Filtering (CF) Recommender System (RS) context, are those who can exert considerable influence over the recommendation outcomes presented to other users. RS operators encourage the existence of power user communities and leverage them to help fellow users make informed purchase decisions. Thus, RS research in this area has focused on power user selection and utilization to address challenges such as rating sparsity for new items or users. But, as ever, there remains the potential for corruption by power users who provide biased ratings. And because of the influence that power users wield, biased ratings they provide can have significant impacts on RS accuracy and robustness. In order to better understand this problem and develop solution strategies, our research is investigating the impact on RS predictions and top-N recommendation lists when power users provide biased ratings. The open areas of research we have explored are analyzing and evaluating power user selection techniques, statistically characterizing power users in order to create attack profiles, mounting power user attacks on new items, and using accuracy and robustness metrics to evaluate power user attacks. In the future, we plan to extend our initial research in power user selection, characterization, and evaluation, as well as generate attack profiles based on power user characteristics, mount power user attacks on user-based, item-based, and SVD-based CF systems, evaluate power user attacks, and generalize our work across different domains.