Open Access
ARTICLE
Adversarial Attacks on Content-Based Filtering Journal Recommender Systems
Zhaoquan Gu1, Yinyin Cai1, Sheng Wang1, Mohan Li1, *, Jing Qiu1, Shen Su1, Xiaojiang Du1, Zhihong Tian1
1 Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China.
2 Department of Computer and Information Sciences, Temple University, Philadelphia, USA.
* Corresponding Author: Mohan Li. Email: .
Computers, Materials & Continua 2020, 64(3), 1755-1770. https://doi.org/10.32604/cmc.2020.010739
Received 24 March 2020; Accepted 28 April 2020; Issue published 30 June 2020
Abstract
Recommender systems are very useful for people to explore what they really
need. Academic papers are important achievements for researchers and they often have a
great deal of choice to submit their papers. In order to improve the efficiency of selecting
the most suitable journals for publishing their works, journal recommender systems (JRS)
can automatically provide a small number of candidate journals based on key information
such as the title and the abstract. However, users or journal owners may attack the system
for their own purposes. In this paper, we discuss about the adversarial attacks against
content-based filtering JRS. We propose both targeted attack method that makes some
target journals appear more often in the system and non-targeted attack method that
makes the system provide incorrect recommendations. We also conduct extensive
experiments to validate the proposed methods. We hope this paper could help improve
JRS by realizing the existence of such adversarial attacks.
Keywords
Cite This Article
APA Style
Gu, Z., Cai, Y., Wang, S., Li, M., Qiu, J. et al. (2020). Adversarial attacks on content-based filtering journal recommender systems. Computers, Materials & Continua, 64(3), 1755-1770. https://doi.org/10.32604/cmc.2020.010739
Vancouver Style
Gu Z, Cai Y, Wang S, Li M, Qiu J, Su S, et al. Adversarial attacks on content-based filtering journal recommender systems. Comput Mater Contin. 2020;64(3):1755-1770 https://doi.org/10.32604/cmc.2020.010739
IEEE Style
Z. Gu et al., "Adversarial Attacks on Content-Based Filtering Journal Recommender Systems," Comput. Mater. Contin., vol. 64, no. 3, pp. 1755-1770. 2020. https://doi.org/10.32604/cmc.2020.010739
Citations