Enhancing the robustness of recommender systems against spammers

• Published in: PloS one Vol. 13; no. 11; p. e0206458
• Main Authors: Zhang, Chengjun, Liu, Jin, Qu, Yanzhen, Han, Tianqi, Ge, Xujun, Zeng, An
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.11.2018; Public Library of Science (PLoS)
• Subjects: Algorithms; Artificial intelligence; Behavior; Biology and Life Sciences; Collaboration; Computer and Information Sciences; Data mining; False information; Information overload; Information science; International conferences; Internet; Knowledge discovery; Methods; Multimedia; On-line systems; Physical Sciences; Recommender systems; Research and Analysis Methods; Robustness; Social network analysis; Social networks; Social Sciences; Spamming; Beijing China; Colorado; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0206458

The accuracy and diversity of recommendation algorithms have always been the research hotspot of recommender systems. A good recommender system should not only have high accuracy and diversity, but also have adequate robustness against spammer attacks. However, the issue of recommendation robustness has received relatively little attention in the literature. In this paper, we systematically study the influences of different spammer behaviors on the recommendation results in various recommendation algorithms. We further propose an improved algorithm by incorporating the inner-similarity of user's purchased items in the classic KNN approach. The new algorithm effectively enhances the robustness against spammer attacks and thus outperforms traditional algorithms in recommendation accuracy and diversity when spammers exist in the online commercial systems.