ROBUREC: Building a Robust Recommender using Autoencoders with Anomaly Detection

Author:

Aly Ahmed1ORCID,Nawara Dina1ORCID,Kashef Rasha1ORCID

Affiliation:

1. Electrical, computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, Ontario, Canada

Funder

Toronto metropolitan university

Publisher

ACM

Reference41 articles.

1. B. C. Kwon, S.-H. Kim, T. Duket, A. Catalán, and J. S. Yi, "Do People Really Experience Information Overload While Reading Online Reviews?," International Journal of Human-Computer Interaction, vol. 31, 2015.

2. C. Li and Z. Luo, "Detection of shilling attacks in collaborative filtering recommender systems," in 2011 International Conference of Soft Computing and Pattern Recognition (SoCPaR), 2011.

3. L. Nguyen and T. Ishigaki, "D2D-TM: A Cycle VAE-GAN for Multi-Domain Collaborative Filtering," in 2019 IEEE International Conference on Big Data (Big Data), 2019.

4. D. Liang, R. G. Krishnan, M. D. Hoffman and T. Jebara, "Variational Autoencoders for Collaborative Filtering," in WWW '18: Proceedings of the 2018 World Wide Web Conference, 2018.

5. F. Zhang and S. Wang, "Detecting Group Shilling Attacks in Online Recommender Systems Based on Bisecting K-Means Clustering," IEEE Transactions on Computational Social Systems, vol. 7, no. 5, 2020.

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