Shilling attack detection in recommender systems via selecting patterns analysis

Wentao Li, Min Gao, Hua Li, Jun Zeng, Qingyu Xiong, Sachio Hirokawa

    Research output: Contribution to journalArticlepeer-review

    9 Citations (Scopus)

    Abstract

    Collaborative filtering (CF) has been widely used in recommender systems to generate personalized recommendations. However, recommender systems using CF are vulnerable to shilling attacks, in which attackers inject fake profiles to manipulate recommendation results. Thus, shilling attacks pose a threat to the credibility of recommender systems. Previous studies mainly derive features from characteristics of item ratings in user profiles to detect attackers, but the methods suffer from low accuracy when attackers adopt new rating patterns. To overcome this drawback, we derive features from properties of item popularity in user profiles, which are determined by users' different selecting patterns. This feature extraction method is based on the prior knowledge that attackers select items to rate with man-made rules while normal users do this according to their inner preferences. Then, machine learning classification approaches are exploited to make use of these features to detect and remove attackers. Experiment results on the MovieLens dataset and Amazon review dataset show that our proposed method improves detection performance. In addition, the results justify the practical value of features derived from selecting patterns.

    Original languageEnglish
    Pages (from-to)2600-2611
    Number of pages12
    JournalIEICE Transactions on Information and Systems
    VolumeE99D
    Issue number10
    DOIs
    Publication statusPublished - Oct 2016

    All Science Journal Classification (ASJC) codes

    • Software
    • Hardware and Architecture
    • Computer Vision and Pattern Recognition
    • Electrical and Electronic Engineering
    • Artificial Intelligence

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