Adversarial Learning for Recommendation: Applications for Security and Generative Tasks — Concept to Code
Author:
Affiliation:
1. Polytechnic University of Bari, Italy
2. SisInf Lab Department of Electrical Engineering and Information Technology Polytechnic University of Bari, Italy
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3383313.3411447
Reference28 articles.
1. SAShA: Semantic-Aware Shilling Attacks on Recommender Systems Exploiting Knowledge Graphs
2. RecGAN
3. Dong-Kyu Chae Jin-Soo Kang Sang-Wook Kim and Jaeho Choi. 2019. Rating Augmentation with Generative Adversarial Networks towards Accurate Collaborative Filtering. In WWW. ACM 2616–2622. Dong-Kyu Chae Jin-Soo Kang Sang-Wook Kim and Jaeho Choi. 2019. Rating Augmentation with Generative Adversarial Networks towards Accurate Collaborative Filtering. In WWW. ACM 2616–2622.
4. CFGAN
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1. Analysis of Recommender System Using Generative Artificial Intelligence: A Systematic Literature Review;IEEE Access;2024
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