@Article{cmc.2020.010739,
AUTHOR = {Zhaoquan Gu, Yinyin Cai, Sheng Wang, Mohan Li, Jing Qiu, Shen Su, Xiaojiang Du, Zhihong Tian},
TITLE = {Adversarial Attacks on Content-Based Filtering Journal  Recommender Systems},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {3},
PAGES = {1755--1770},
URL = {http://www.techscience.com/cmc/v64n3/39457},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.010739}
}