A Research on Shilling Attacks Based on Variational graph auto-encoders for Improving the Robustness of Recommendation Systems-Reference-Cited by-同舟云学术

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A Research on Shilling Attacks Based on Variational graph auto-encoders for Improving the Robustness of Recommendation Systems

Published:2024-05-10 Issue: Volume: Page:120-126
ISSN:
Container-title:Proceedings of the 2024 International Conference on Generative Artificial Intelligence and Information Security
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Author:

Lu Qianlong¹^ORCID,Gao Maoting¹^ORCID

Affiliation:

1. College of Information Engineering, Shanghai Maritime University, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3665348.3665370

Reference24 articles.

1. Qi, L., He, Q., Chen, F., Zhang, X., Dou, W., & Ni, Q. 2020. Data-driven web APIs recommendation for building web applications. IEEE transactions on big data, 8(3), 685-698.

2. A Survey on Adversarial Recommender Systems

3. Singh, P. K., Pramanik, P. K. D., & Choudhury, P. 2020. Collaborative filtering in recommender systems: Technicalities, challenges, applications, and research trends. In New Age Analytics (pp. 183-215). Apple Academic Press.

4. Should we worry about filter bubbles?. Internet Policy Review;Zuiderveen Borgesius F.;Journal on Internet Regulation, 5(1).,2016

5. Shilling recommender systems for fun and profit

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