Adversarial Attacks for Black-Box Recommender Systems via Copying Transferable Cross-Domain User Profiles-Reference-Cited by-同舟云学术

Adversarial Attacks for Black-Box Recommender Systems via Copying Transferable Cross-Domain User Profiles

Published:2023-12-01 Issue:12 Volume:35 Page:12415-12429
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Fan Wenqi¹^ORCID,Zhao Xiangyu²^ORCID,Li Qing¹^ORCID,Derr Tyler³^ORCID,Ma Yao⁴^ORCID,Liu Hui⁵^ORCID,Wang Jianping²^ORCID,Tang Jiliang⁵^ORCID

Affiliation:

1. Department of Computing, The Hong Kong Polytechnic University, Hong Kong

2. City University of Hong Kong, Hong Kong

3. Vanderbilt University, Nashville, TN, USA

4. New Jersey Institute of Technology, Newark, NJ, USA

5. Michigan State University, Lansing, MI, USA

Funder

National Natural Science Foundation of China

General Research Funds from the Hong Kong Research Grants Council

Hong Kong Polytechnic University

Research Collaborative

SHTM Interdisciplinary Large

Hong Kong Research Grant Council

APRC-CityU New Research Initiatives

New Faculty of City University of Hong Kong

SIRG-CityU Strategic Interdisciplinary Research

HKIDS Early Career Research

Huawei Innovation Research Program

CCF-Ant Research Fund

Ant Group Research Fund

National Science Foundation

Army Research Office

Home Depot

Cisco Systems

Amazon Faculty Award

Johnson&Johnson

SNAP

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

https://ieeexplore.ieee.org/ielam/69/10311056/10114977-aam.pdf

Reference59 articles.

1. Robust Physical-World Attacks on Deep Learning Visual Classification

2. Deep reinforcement learning for page-wise recommendations

3. Deep reinforcement learning in large discrete action spaces;dulac-arnold,2015

4. "Deep reinforcement learning for search, recommendation, and online advertising: a survey" by Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin with Martin Vesely as coordinator

5. Attacking Black-box Recommendations via Copying Cross-domain User Profiles

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