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    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

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1755-1770, 2020, DOI:10.32604/cmc.2020.010739 - 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 More >

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