The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Shilling recommender systems for fun and profit
Abstract
Recommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. One application in which they have become particularly common is in e-commerce, where recommendation of items can often help a customer find what she is interested in and, therefore can help drive sales. Unscrupulous producers in the never-ending quest for market penetration may find it profitable to shill recommender systems by lying to the systems in order to have their products recommended more often than those of their competitors. This paper explores four open questions that may affect the effectiveness of such shilling attacks: which recommender algorithm is being used, whether the application is producing recommendations or predictions, how detectable the attacks are by the operator of the system, and what the properties are of the items being attacked. The questions are explored experimentally on a large data set of movie ratings. Taken together, the results of the paper suggest that new ways must be used to evaluate and detect shilling attacks on recommender systems.
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Citations
Cites
Machines and Mindlessness: Social Responses to Computers
Nass, C., & Moon, Y. (2000) · Journal of Social Issues · doi:10.1111/0022-4537.00153 · Cited by 4,022
Cited by
O'Donovan, J., & Smyth, B. (2005) · Proceedings of the 10th international conference on Intelligent user interfaces · doi:10.1145/1040830.1040870 · Cited by 865
hPSD: A Hybrid PU-Learning-Based Spammer Detection Model for Product Reviews
Wu, Z., Cao, J., Wang, Y., Wang, Y., Zhang, L., & Wu, J. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2877161 · Cited by 166
My Mobile Music: An Adaptive Personalization System for Digital Audio Players
Chung, T. S., Rust, R. T., & Wedel, M. (2009) · Marketing Science · doi:10.1287/mksc.1080.0371 · Cited by 119
Shilling Black-Box Recommender Systems by Learning to Generate Fake User Profiles
Lin, C., Chen, S., Zeng, M., Zhang, S., Gao, M., & Li, H. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3183210 · Cited by 62
Data Poisoning Attack against Recommender System Using Incomplete and Perturbed Data
Zhang, H., Tian, C., Li, Y., Su, L., Yang, N., Zhao, W. X., & Gao, J. (2021) · Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining · doi:10.1145/3447548.3467233 · Cited by 55
Fan, W., Zhao, X., Li, Q., Derr, T., Ma, Y., Liu, H., Wang, J., & Tang, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3272652 · Cited by 41
Anelli, V. W., Deldjoo, Y., Di Noia, T., Malitesta, D., & Merra, F. A. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462848 · Cited by 31
Gray-Box Shilling Attack: An Adversarial Learning Approach
Wang, Z., Gao, M., Li, J., Zhang, J., & Zhong, J. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3512352 · Cited by 28
Targeted Shilling Attacks on GNN-based Recommender Systems
Guo, S., Bai, T., & Deng, W. (2023) · Proceedings of the 32nd ACM International Conference on Information and Knowledge Management · doi:10.1145/3583780.3615073 · Cited by 23
On the Detection of Shilling Attacks in Federated Collaborative Filtering
Jiang, Y., Zhou, Y., Wu, D., Li, C., & Wang, Y. (2020) · 2020 International Symposium on Reliable Distributed Systems (SRDS) · doi:10.1109/srds51746.2020.00026 · Cited by 22
The Effect of Suspicious Profiles on People Recommenders
Pizzato, L. A., Akehurst, J., Silvestrini, C., Yacef, K., Koprinska, I., & Kay, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-31454-4_19 · Cited by 8
Privacy-Preserving Recommendation with Debiased Obfuscaiton
Lin, C., Liu, B., Zhang, X., Wang, Z., Hu, C., & Luo, L. (2022) · 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom56396.2022.00086 · Cited by 4
Metadata
- Authors
- Lam, S. K., & Riedl, J.
- Venue
- Proceedings of the 13th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2004-05-17
- Pages
- 393-402
- OpenAlex
- W2072651985
- Cited by
- 635
- References
- 1
- FWCI
- 21.39
- Type
- conference paper
- Language
- en
- Open access
- closed
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